Transcript Observing System Simulation Experiments in the Joint Center for Satellite Data Lars Peter Riishojgaard and Michiko Masutani JCSDA WMO OSE Workshop, Geneva 05/2008

Observing System Simulation
Experiments in the Joint Center
for Satellite Data
Lars Peter Riishojgaard and Michiko Masutani
JCSDA
WMO OSE Workshop, Geneva 05/2008
Joint OSSE Team
NCEP:
Michiko Masutani, John S. Woollen, Yucheng Song, Stephen J.
Lord, Zoltan Toth
ECMWF:
Erik Andersson
KNMI:
Ad Stoffelen, Gert-Jan Marseille
JCSDA:
Lars Peter Riishojgaard
NESDIS:
Fuzhong Weng, Tong Zhu, Haibing Sun,
SWA:
G. David Emmitt, Sidney A. Wood, Steven Greco
NASA/GFSC: Ron Errico, Oreste Reale, Runhua Yang, Emily Liu, Joanna Joiner,
Harper Pryor, Alindo Da Silva, Matt McGill,
NOAA/ESRL:Tom Schlatter, Yuanfu Xie, Nikki Prive, Dezso Devenyi, Steve
Weygandt
MSU/GRI: Valentine Anantharaj, Chris Hill, Pat Fitzpatrick,
JMA
Takemasa Miyoshi , Munehiko Yamaguchi
JAMSTEC Takeshi Enomoto
So far most of the work is done by volunteers.
JCSDA: Joint Center for Satellite Data Assimilation
SWA: Simpson Weather Associates
ESRL: Earth System Research Laboratory (formerly FSL, CDC, ETL)
OSSEs
• Observing System Simulation Experiment
– Typically aimed at assessing the impact of a hypothetical data
type on a forecast system
• Not straightforward; EVERYTHING must be simulated
– Simulated atmosphere (“nature run”)
– Simulated reference observations (corresponding to
existing observations)
– Simulate perturbation observations
– (object of study)
– => Costly in terms of computing and manpower
WMO OSE Workshop, Geneva 05/2008
Data assimilation
Nature
(atmospheric state)
Sensors
Assessment
Observations
(RAOB, TOVS,
GEO, surface,
aircraft, etc.)
Analysis
End products
Short range
product
Initial conditions
Forecast
model
OSE, conceptual model
Nature
(atmospheric state)
Sensors
Candidate
observations
(e.g. AIRS)
Assessment
Reference
observations
(RAOB, TOVS,
GEO, surface,
aircraft, etc.)
Analysis
End products
Short range
product
Initial conditions
WMO OSE Workshop, Geneva 05/2008
Forecast
model
OSSE, conceptual model
Nature run
(output from high
resolution, high quality
climate model)
Simulator
Candidate
observations
(e.g. GEO MW)
Assessment
Reference
observations
(RAOB, TOVS,
GEO, surface,
aircraft, etc.)
Analysis
End products
Forecast
products
Initial conditions
WMO OSE Workshop, Geneva 05/2008
Forecast
model
Role of a National OSSE Capability
• Impact assessment of future missions
– Decadal Survey and other science and/or technology
demonstration missions (NASA)
– Future operational systems (NOAA)
• Objective way of establishing scientifically sound and
technically feasible user requirements for observing systems
• Tool for assessing performance impact of engineering decisions
made throughout the development phases of a space program
or system
• Preparation/early learning pre-launch tool for assimilation users
of data from new sensors
WMO OSE Workshop, Geneva 05/2008
Why a Joint OSSE capability?
•
OSSEs are expensive
– Nature run, entire reference observing system, additional
observations must be simulated
– Calibration experiments, perturbation experiments must be
assessed according to standard operational practice and using
operational metrics and tools
•
OSSE-based decisions have many stakeholders
– Decisions on major space systems have important scientific,
technical, financial and political ramifications
– Community ownership and oversight of OSSE capability is
important for maintaining credibility
•
Independent but related data assimilation systems allows us to test
robustness of answers
WMO OSE Workshop, Geneva 05/2008
Main OSSE components
•
Data assimilation system(s)
– NCEP/EMC GFS
– NASA/GMAO GEOS-5
– NCAR WRF-VAR
•
Nature run
– ECMWF
– Plans for embedded WRF Regional NR
•
Simulated observations
– Reference observations
– Perturbation (“candidate”) observations
•
Diagnostics capability
– “Classical” OSE skill metrics
– Adjoint sensitivity studies
WMO OSE Workshop, Geneva 05/2008
ECMWF Nature Run (Erik Andersson)
• Based on recommendations/requirements from JCSDA,
NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL
• “Low Resolution” Nature Run
– Free-running T511 L91 w. 3-hourly dumps
– May 12 2005 through June 1 2006
• Two “High Resolution” periods of 35 days each
– Hurricane season: Starting at 12z September 27,2005,
– Convective precipitation over CONUS: starting at 12Z April 10, 2006
• T799 L91 levels, one-hourly dump
• Initial condition from T511 NR
WMO OSE Workshop, Geneva 05/2008
Nature Run validation
• Purpose is to ensure that pertinent aspects of meteorology
are represented adequately in NR
• Contributions from Emmitt, Errico, Masutani, Prive, Reale,
Terry, Tompkins and many others
•
•
•
•
•
•
Clouds
Precipitation
Extratropical cyclones (tracks, cyclogenesis, cyclolosis)
Tropical cyclones (tracks, intensity)
Mean wind fields
….
WMO OSE Workshop, Geneva 05/2008
Initial Nature Run validation
Study of drift in NR
Michiko Masutani (NCEP)
Area averaged precipitation
Tropics
Zonal wind June 2006
By Juan Carlos Jusem (NASA/GSFC)
NCEP reanalysis
Nature Run
Convective precipitation
Large Scale precipitation
Total precipitation
Two to three weeks spinup in tropical precipitation.
- Michiko Masutani (NCEP/EMC)
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.
Extratropical Cyclone Statistics
Joe Terry
NASA/GSFC
1) Extract cyclone information using Goddard’s objective cyclone tracker
• Nature Run
• One degree operational NCEP analyses (from several surrounding years)
• NCEP reanalysis for specific years (La Nina, El Nino, FGGE)
2) Produce diagnostics using the cyclone track information
(comparisons between Nature Run and NCEP analyses for same month)
• Distribution of cyclone strength across pressure spectrum
• Cyclone lifespan
• Cyclone deepening
• Regions of cyclogenesis and cyclolysis
• Distributions of cyclone speed and direction
Evaluation of Cloud
Simpson weather associates
Total Cloud Cover (Land and Ocean)
100
90
80
Total Cloud Cover (%)
70
60
50
40
30
- NR
- ISCCP
- WWMCA
-- HIRS
20
10
0
-90
-60
-30
0
Latitude
30
60
90
Case Events Identified from ECMWF HRNR
(Plotted from 1x1 data)
May 2-4: squall line affecting all points along US Gulf coast
MSLP (hPa)
May 7-8: decaying squall line over TX
3-h convective precipitation (mm)
.
Oct 10-11: squall line / tropical wave
Christopher M. Hill, Patrick J. Fitzpatrick, Valentine G. Anantharaj
Mississippi State University
Simulation of observations
• Conventional observations (non-radiances)
– “Resample NR at OBS locations and add error”
– Problem areas:
• Atmospheric state affects sampling for RAOBS,
Aircraft observations, satellite AMVs, wind lidars, etc.
• Correlated observations errors
– J. Woollen (NCEP), R. Errico (GMAO)
• Radiance observations
– “Forward radiative transfer on NR input profiles”
– Problem areas:
• Treatment of clouds has substantial impact on
availability and quality of observations
• Desire to avoid “identical twin” RTMs
– H. Sun (NESDIS), R. Errico (GMAO)
WMO OSE Workshop, Geneva 05/2008
OBS91L
Jack Woollen (NCEP/EMC)
For development purposes, 91-level NR variables are processed at NCEP and
interpolated to observational locations with all the information need to simulate
data (OBS91L).
OBS91L for all footprints of HIRS, AMSU, GOES are produced for a few
weeks of the T799 period in October 2005.
Thinned footprints for the entire period.
Thinning of the footprint is based on operational use of radiance data.
The OBS91L are also available for development of a Radiative Transfer Model
(RTM) for development of other forward model.
Radiance Simulation System for OSSE
GMAO, NESDIS, NCEP
Ron Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard
(NASA/GSFC/GMAO)
Tong Zhu, Haibing Sun, Fuzhong Weng (NOAA/NESDIS)
Jack Woollen(NOAA/NCEP)
Other resources and/or advisors David Groff , Paul Van Delst (NCEP)
Yong Han, Fuzhong Weng,Walter Wolf, Cris Barnet, Mark Liu (NESDIS)
Erik Andersson (ECMWF); Roger Saunders (Met Office)
NASA/GMAO developing optimized strategies to simulate complete
set of footprints. This includes development of cloud clearing
algorithm.
NESDIS, NCEP working on thinned data. Full resolution data for
GOES-R. Initial data set (OBS91L) produced by Jack Woollen at
NCEP
Existing instruments experiments must be simulated for
control and calibration and development of DAS and RTM
Test GOESR,NPOESS, and other future satellite data
OBS91L
Jack Woollen (NCEP/EMC)
For development purposes, 91-level NR variables are processed at NCEP and
interpolated to observational locations with all the information need to simulate
data (OBS91L).
OBS91L for all foot prints of HIRS, AMSU, GOES are produced for a few
weeks of the T799 period in October 2005.
Thinned foot prints for the entire period.
Thinning of the foot print is based on operational use of radiance data.
The OBS91L are also available for development of a Radiative Transfer Model
(RTM) for development of other forward model.
Radiance Simulation System for OSSE
GMAO, NESDIS, NCEP
Tong Zhu, Haibing Sun, Fuzhong Weng
(NOAA/NESDIS)
Jack Woollen(NOAA/NCEP)
Ron Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard
(NASA/GSFC/GMAO)
Other resources and/or advisors David Groff , Paul Van Delst (NCEP)
Yong Han, Walter Wolf, Cris Bernet,, Mark Liu, M.-J. Kim, Tom Kleespies, (NESDIS)
Erik Andersson (ECMWF); Roger Saunders (Met Office)
OBS91L is produced by Jack Woollen at NCEP
NASA/GMAO is developing best strategies to simulate and work on complete
foot prints. This include development of cloud clearing algorithm.
NESDIS and NCEP are working on thinned data. Full resolution data for
GOESR.
Existing instruments experiments must be simulated for
control and calibration and development of DAS and RTM
Test GOESR,NPOESS, and other future satellite data
Simulation of GOES-R ABI radiances for OSSE
Tong Zhu et al. : 5GOESR P1.31 at AMS annual meeting
http://www.emc.ncep.noaa.gov/research/JointOSSEs/publications/AMS_Jan2008/Poster-88thAMS2008-P1.31-OSSEABI.ppt
Simulated from T511 NR. GOES data will be simulated to investigate its
data impact
Current set of “prototype” simulated observations at
the GMAO derived from the ECMWF Nature Run
HIRS3, HIRS2, AMSU-A/B, AIRS,+ Conventional Obs.
The satellite data is thinned, but less so than used operationally.
Thinning is based on time of report and defined effect of clouds.
Clouds are treated as black at their tops, when defined as present.
The presence of clouds affecting IR is determined by a tunable
stochastic function using NR-provided cloud fractions. This
function is intended to account for holes in grid-boxes and
allows simple tuning for possible unrealism in the NR cloud distribution.
The same CRTM is used as in GSI (the only RTM available to us).
Locations of cloud track winds are independent of NR clouds.
The list of simulated obs. types will be expanded along with the realism
of the simulations and their associated errors as resources permit.
We hope to have a suitably tuned (validated) set of “prototype” simulated
obs. available by the end of Sept. 2009.
Slide from Errico
OSSEs planned
OSSEs to investigate data impact of GOES
and prepared for GOES-R
Tong Zhu, Fuzhon Weng, J. Woollen (NCEP) M.Masutani(NCEP) and more
OSSE to evaluate UAS
OSSE to evaluate DWL
N. Prive(ESRL), Y. Xie(ESRL)
possible at NCEP and others
M.Masutani(NCEP), L. P Riishojgaard
(JCDA), NOAA/ESRL, and others
OSSEs for THORPEX T-PARC
Evaluation and development of targeted observation
Z. Toth, Yucheng Song (NCEP) and other THORPEX team
Regional OSSEs to Evaluate ATMS and CrIS
Observations
Cris M. Hill, Pat. J. Fitzpatrick, Val. G. Anantharaj GRI- Mississippi State University (MSS)
Lars-Peter Riishojgaard (NASA/GMAO, JCSDA)
Next steps
• Calibration; impact of main classes of observation
should mimic what is seen in operational OSEs
– GMAO will calibrate GEOS-5 using adjoint sensitivity tools
– EMC will calibrate GFS OSSE using OSEs
– Goal is to have calibrated systems available for actual OSSEs
by late summer 2008
• Funding
– NASA ROSES proposal
– NOAA JCSDA-led budget initiative
– ESA/EUMETSAT encouraged by ADMAG to participate
WMO OSE Workshop, Geneva 05/2008
Summary
• OSSEs are expensive, but can be a cost-effective way to
optimize investment in future observing systems
• OSSE capability should be multi-agency, community owned
to avoid conflict of interest
• Independent but related data assimilation systems allows us
to test robustness of answers
• Joint OSSE collaboration remains only partially funded but
appears to be headed in right direction
WMO OSE Workshop, Geneva 05/2008