Transcript THORPEX Data Assimilation and Observing Strategies Working

Lessons learned from THORPEX
THORPEX working group on Data
Assimilation and Observing Strategies
Florence Rabier
Pierre Gauthier
Carla Cardinali
Ron Gelaro
Ko Koizumi
Rolf Langland
Andrew Lorenc
Peter Steinle
Mickael Tsyrulnikov
(Météo-France and CNRS, France, Co-chair)
(UQAM, Canada,Co-chair)
(ECMWF, Int)
(GMAO, USA)
(JMA, Japan)
(NRL, USA)
(Met Office, UK)
(BMRC, Australia)
(HRCR, Russia)
Nonlinear Processes in Geophysics, 15, 1-14, 2008
New WG being formed, including Observing Systems
THORPEX and the DAOS-WG
• “THORPEX: a Global Atmospheric Research Programme”
established in 2003 by WMO.
• Mission statement: “Accelerating improvements in the
accuracy of high-impact 1-14 day weather forecasts for the
benefit of society and the economy”
• Design and demonstration of interactive forecast systems:
enhancements to the observations usage in “sensitive
regions”
• Perform THORPEX Observing-System Tests and Regional
field Campaigns to test and evaluate experimental remotesensing and in-situ observing systems
• DAOS-WG: evaluate and improve the impact of observations
Outline
• Context
• Main objectives
– Assess impact of observations and observing system
design
– Targeting strategies
– Improved use of observations
• Illustrations from field campaigns (AMMA…),
the Intercomparison experiment and the WMO
Data Impact Workshop
(http://www.wmo.int/pages/prog/www/OSY/Reports/NWP-4_Geneva2008_index.html)
Large number of data and different data sources
Assessing the impact of
observations
•
•
•
•
•
OSEs
OSSEs
DFS
Error variance reduction
Sensitivity to observations
Winter results: Baseline – Control (Z500)
Impact of terrestrial, non-climate, observations
NH
ECMWF
EUR
Differences in RMS errors and significance bars for each forecast range
Control-Baseline (Z500)
Normalised forecast error difference, Day-3
Geographical distribution of error reduction
ECMWF
Neutral
Case
impact
A few
hours
6 hours
12 hours
Radiosonde
Aircraft
Buoys
Northern
Hemisphere
Extra-tropics
AIRS
IASI
AMSU/A
GPS-RO
SCAT
AMV
SSMI
Radiosonde
Aircraft
Buoys
AIRS
Tropics
IASI
AMSU/A
GPS-RO
SCAT
AMV
SSMI
Radiosonde
Aircraft
Buoys
Southern
Hemisphere
Extra-tropics
AIRS
IASI
AMSU/A
GPSRO
SCAT
AMV
SSMI
Synthesis of all results after WMO workshop
OSSE, conceptual model
Nature run
(output from high
resolution, high quality
climate model)
Simulator
Candidate
observations
(e.g. GEO MW)
JCSDA
Assessment
Reference
observations
(RAOB, TOVS,
GEO, surface,
aircraft, etc.)
Analysis
End products
Forecast
products
Initial conditions
Forecast
model
Tropical cyclone NR validation
Preliminary findings suggest good
degree of realism of Atlantic tropical
cyclones in ECMWF NR.
HL vortices: vertical structure
Vertical structure of a HL vortex shows distinct eyelike feature and prominent warm core; low-level
wind speeds exceed 55 m/s
Reale O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J. C. Jusem
(2007), Preliminary evaluation of the European Centre for Medium-Range
Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic and
African monsoon region, Geophys. Res. Lett., 34, L22810,
doi:10.1029/2007GL031640.
DFS: Information content by area
DFS= Tr(HK)=Tr(I-AB-1)
M-F
Ensemble variational assimilation
at Météo-France
(From Ehrendorfer, 2006)
• Ensemble assimilation : simulation of the
joint evolution of analysis, background and
observation errors:
ea = (I – KH) eb + K eo.
Ensemble sb – sa with
energy norm
One month statistics (January 2007) at 00UTC
6 member 3D-Var FGAT ensemble
Desroziers, M-F
Sensitivity to Observation
(Langland and Baker, 2004)
14
OBSERVATIONS
ASSIMILATED
e30
e24
00UTC
+ 24h
Observations move the model state from the “background” trajectory
to the new “analysis” trajectory
The difference in forecast error norms, e24  e30 , is due to the
combined impact of all observations assimilated at 00UTC
Estimating Observation Impact
Forecast error measure (dry energy, sfc–140 hPa):
e  (x0f  x v )T C (x0f  x v )
Taylor expansion of change in e due to change in x0 :
e 1  2e
1  3e 2
T
 e   x0 (


x


x

...)

(

x
)
0
0
0 g
x0 2 x02
6 x30
Analysis equation allows transformation to observation-space:
 x0  xa  x b  K y
3rd order approximation of  e in observation space:
 e  ( y )T K T [MTbC(x bf  x v )  MTaC(xaf  x v )]  ( y )T ~
g3
analysis adjoint
model adjoint
…summed
observation
impact
Properties of the Impact Estimate
 e  ( y )T ~
g3
 The impact of arbitrary subsets of observations (e.g. instrument
type, channel, location) can be easily quantified by summing only the
terms involving the desired elements of  y .
 The “weight” vector ~
g3 is computed only once, and involves the
entire set of observations…removing or changing the properties of
one observation changes the weight of all other observations.
 Valid forecast range limited by tangent linear assumption for M
T
 e  0 …the observation improves the forecast
 e  0 …the observation degrades the forecast
…see Langland and Baker (2004), Errico (2007), Gelaro et al. (2007)
Forecast error norms and differences
Global
forecast error
total energy
norm (J kg-1)
Forecasts from 0600 and 1800 UTC
have larger errors
e30
Forecast errors on
background-trajectories
e24
Forecast errors on
analysis-trajectories
e24 – e30 (adjoint)
e24 – e30 (nonlinear)
NRL
NAVDAS-NOGAPS
Percent of observations that produce forecast error
reduction (e24 – e30 < 0)
NRL
AMMA RAOB
Temperature Ob Impacts
May-Oct 2006
BANAKO:61291 SUM= -0.5755 J kg-1
TAMANASET:60680 SUM= -0.2791 J kg-1
NRL
Example : AMV impact problem
Date: Jan-Feb 2006
Issue: Non-beneficial
impact from MTSAT
AMVs at edge of
coverage area
Action Taken: Data
provider identified
problem with wind
processing algorithm.
NRL
Comparison and Interpretation of ADJ and OSE Results
…a few things to keep in mind…
 The ADJ measures the impacts of observations in the context of all
other observations present in the assimilation system, while the OSE
changes/degrades the system ( K differs for each OSE member)
 The ADJ measures the impact of observations in each analysis cycle
separately and against the control background, while the OSE measures
the impact of removing information from both the background and
analysis in a cumulative manner
 Comparison is restricted to the forecast range and metric for which
the adjoint results are valid on the one hand (24h-energy in this
study) and to the observing systems tested in the OSE on the other
Gelaro
Combined Use of ADJ and OSEs (Gelaro et al., 2008)
…ADJ applied to various OSE members to examine how the mix of
observations influences their impacts
Removal of AMSUA results in large increase in AIRS (and other) impacts
Removal of AIRS results in significant increase in AMSUA impact
Removal of raobs results in significant increase in AMSUA, aircraft and
other impacts (but not AIRS)
NASA, GMAO
Total observation impact at 00 UTC
NAVDAS 24h Ob Impact Jan2007 00z+06z
Ships
SatWind
SSMIspd
RaobDsnd
Qscat
Windsat
MODIS
LandSfc
Aircraft
AMSUA
-100
-80
-60
-40
-20
0
Targeting strategies
Evaluating and improving targeting strategies
Observation time
Verification time
Adjoint model or
Ensemble Transform
• Select additional observations or optimize the use of satellite
sensors (sampling rate, thinning, chanel selection…)
• Results depend on method, flow regimes
• To be extended to Tropics (model error), evaluation at finer
scales
A-TReC (Atlantic THORPEX Regional
Campaign) Oct15-Dec17 2003
• The ATREC was led by EUCOS in the context of THORPEX. It
involved UK Met office, ECMWF, Meteo-France, NRL, NASA, U
of North Dakota, Meteorological Service of Canada, NCEP, FSL,
NCAR and U of Miami
• A variety of observing platforms were deployed. AMDAR (550),
ASAP ships (13), radiosondes (66), GOES rapid-scan winds and
dropsondes.
70
With 850 hPa
Without 850 hPa
With 500 hPa
Without 500 hPa
RMS (m)
60
50
Geopotential forecast error for
the ATReC area
(wrt analyses)
40
30
20
10
0
6
18
30
42
54
66
78
forecast range (hours)
90
Fourrié, et al, M-F
Impact of targeted obs
•
Targeting is possible and successful – mid-latitude targeted
observations are about twice as effective as random observations.
•
Improvements to DA methods should improve the assimilation of all
observations in sensitive regions, including targeted obs, but the
statistical basis still means that only just over 50% will have a positive
impact.
•
Improvements to targeting methods are possible (e.g. longer leads for
large areas) but the statistical basis means that impacts on scores will
vary.
•
Thanks to the general improvement of operational NWP, the average
impact of individual observing systems is decreasing.
•
Targeting alone is unlikely to significantly accelerate improvements in
the accuracy of 1 to 14-day weather forecasts compared to other
improvements over the THORPEX period in NWP and satellites.
Improving the use of
observations
• Extending the use of satellite data
• Bias correction
Improved representation of surface emissivity for
the assimilation of microwave observations
•Dynamical approach for the estimation
of the emissivity from Satellite
observations over land (Karbou 2006)
•The estimation of emissivity has been
adapted to Antarctica : snow and sea
ice surfaces
Karbou, M-F
Comparison of the new emissivity calculation with the old
one, over sea ice
New
Old
Fg-departure (K) (obs- first guess) histograms for
AMSU-A, ch4 (July 2007)
Fg-departure (K) (obs- first guess) histograms for
AMSU-B, ch2 (July 2007)
Use of additional microwave data
CONTROL
Density
of data
Being
actively
EXP
AMSUB- Ch3
assimilated
AMSUA- Ch5
Bouchard, Karbou,
M-F
AMMA: The African
Monsoon
Multidisciplinary
Analysis
Better understand the mechanisms of the African monsoon and prevent
dramatic situations
(Redelsperger et al, 2006)
Enhanced observations over West Africa in 2006
In particular, major effort to enhance the radiosonde network
(Parker et al, 2008)
Impact of using the AMMA radiosonde
dataset
•
New radiosonde stations
•
Enhanced time sampling
•
AMMA database: additional
data which were not
received in real time +
enhanced vertical
resolution
•
Bias correction for RH
developed at ECMWF
(Agusti-Panareda et al)
•
Data impact studies
Number of soundings provided on GTS in 2006 and 2005
With various datasets,
Period: 15 July- 15 September, 0 and 12 UTC
With and without RH bias
correction
Impact on quantitative prediction
of precipitation over Africa
CNTR: data from GTS
AMMA: from the AMMA database
AMMABC: AMMA + bias correction
PreAMMA: with a 2005 network
Higher scores
for AMMABC
NOAMMA: No Radiosonde data
Faccani et al, M-F
Lowest scores for
NO AMMA
Work performed and lessons learnt
• Impact of observations
– Guidance for observation campaigns and the configuration of the
Global Observing system
– Assessment of the value of targeted observations (papers by
Buizza, Cardinali, Kelly, in QJRMS)
– Evaluation of observation impact with different systems (A-TReC,
AMMA…). Need for relevant bias correction.
– Intercomparison experiment for sensitivity to observations
• Improving the use of satellite data
– Extend our use of satellite data (density, cloudy/rainy, over land)
• Important to study different methods and different
systems to draw relevant conclusions