Lessons learned from THORPEX THORPEX working group on Data Assimilation and Observing Strategies

1
Lessons learned from THORPEX THORPEX working
group on Data Assimilation and Observing
Strategies

• Florence Rabier (Météo-France and CNRS, France,
  Co-chair)
• Pierre Gauthier (UQAM, Canada,Co-chair)
• Carla Cardinali (ECMWF, Int)
• Ron Gelaro (GMAO, USA)
• Ko Koizumi (JMA, Japan)
• Rolf Langland (NRL, USA)
• Andrew Lorenc (Met Office, UK)
• Peter Steinle (BMRC, Australia)
• Mickael Tsyrulnikov (HRCR, Russia)
• Nonlinear Processes in Geophysics, 15, 1-14, 2008
• New WG being formed, including Observing Systems

2
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 remote-sensing and in-situ observing
  systems
• DAOS-WG evaluate and improve the impact of
  observations

3
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/NW
  P-4_Geneva2008_index.html)

4
Large number of data and different data sources
5
Assessing the impact of observations

• OSEs
• OSSEs
• DFS
• Error variance reduction
• Sensitivity to observations

6
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
7
Control-Baseline (Z500)Normalised forecast error
difference, Day-3
Geographical distribution of error reduction
ECMWF
8
Neutral Neutral Case impact Case impact A few hours A few hours 6 hours 6 hours 12 hours 12 hours
NorthernHemisphereExtra-tropics Radiosonde
NorthernHemisphereExtra-tropics Aircraft
NorthernHemisphereExtra-tropics Buoys
NorthernHemisphereExtra-tropics AIRS
NorthernHemisphereExtra-tropics IASI
NorthernHemisphereExtra-tropics AMSU/A
NorthernHemisphereExtra-tropics GPS-RO
NorthernHemisphereExtra-tropics SCAT
NorthernHemisphereExtra-tropics AMV
NorthernHemisphereExtra-tropics SSMI
Tropics Radiosonde
Tropics Aircraft
Tropics Buoys
Tropics AIRS
Tropics IASI
Tropics AMSU/A
Tropics GPS-RO
Tropics SCAT
Tropics AMV
Tropics SSMI
SouthernHemisphereExtra-tropics Radiosonde
SouthernHemisphereExtra-tropics Aircraft
SouthernHemisphereExtra-tropics Buoys
SouthernHemisphereExtra-tropics AIRS
SouthernHemisphereExtra-tropics IASI
SouthernHemisphereExtra-tropics AMSU/A
SouthernHemisphereExtra-tropics GPSRO
SouthernHemisphereExtra-tropics SCAT
SouthernHemisphereExtra-tropics AMV
SouthernHemisphereExtra-tropics SSMI
Synthesis of all results after WMO workshop
9
OSSE, conceptual model
Nature run (output from high resolution, high
quality climate model)
Assessment
End products
Simulator
Reference observations (RAOB, TOVS, GEO,
surface, aircraft, etc.)
Forecast products
Analysis
Forecast model
Candidate observations (e.g. GEO MW)
Initial conditions
JCSDA
10
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
eye-like 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,
doi10.1029/2007GL031640.
11
DFS Information content by area
DFS Tr(HK)Tr(I-AB-1)
M-F
12
Ensemble variational assimilationat Météo-France
(From Ehrendorfer, 2006)

• Ensemble assimilation simulation of the joint
  evolution of analysis, background and observation
  errors
• ea (I KH) eb K eo.
• Observations are explicited perturbed, while
  backgrounds are implicitly perturbed through
  cycling.

13
Ensemble sb sa with energy norm
One month statistics (January 2007) at 00UTC 6
member 3D-Var FGAT ensemble
Desroziers, M-F
14
Sensitivity to Observation (Langland and Baker,
2004)
14
Observations move the model state from the
background trajectory to the new analysis
trajectory The difference in forecast error
norms, , is due to the combined
impact of all observations assimilated at 00UTC
15
Estimating Observation Impact
Forecast error measure (dry energy, sfc140 hPa)
summed observation impact
model adjoint
analysis adjoint
16
Properties of the Impact Estimate
? 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 .
the observation improves the forecast
the observation degrades the forecast
see Langland and Baker (2004), Errico (2007),
Gelaro et al. (2007)
17
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
18
NAVDAS-NOGAPS Percent of observations that
produce forecast error reduction (e24 e30 lt 0)
NRL
19
AMMA RAOB Temperature Ob Impacts May-Oct 2006
BANAKO61291 SUM -0.5755 J kg-1
TAMANASET60680 SUM -0.2791 J kg-1
NRL
20
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
21
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 ( 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
22
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
23
Total observation impact at 00 UTC
24
Targeting strategies
25
Evaluating and improving targeting strategies
Verification time

• 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

26
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.

Geopotential forecast error for the ATReC area
(wrt analyses)
Fourrié, et al, M-F
27
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.

28
Improving the use of observations

• Extending the use of satellite data
• Bias correction

29

• 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
30

• 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)
31
Use of additional microwave data
CONTROL
Density of data Being actively assimilated
EXP
Bouchard, Karbou, M-F
AMSUB- Ch3
AMSUA- Ch5
32
AMMA The African Monsoon Multidisciplinary
Analysis
Better understand the mechanisms of the African
monsoon and prevent dramatic situations (Redelspe
rger et al, 2006) Enhanced observations over
West Africa in 2006 In particular, major effort
to enhance the radiosonde network (Parker et
al, 2008)
33
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
• With various datasets,
• With and without RH bias correction

Number of soundings provided on GTS in 2006 and
2005 Period 15 July- 15 September, 0 and 12 UTC
34
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 NOAMMA No Radiosonde data
Higher scores for AMMABC
Lowest scores for NO AMMA
Faccani et al, M-F
35
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