JCSDA Briefing

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COSMIC Retreat
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Overview

• Background
• Define JCSDA
• Mission, Vision and Goals
• Progress, Major Accomplishments
• Recent Advances
• GPS/COSMIC Program
• Summary

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JCSDA Mission and Vision

• Mission Accelerate and improve the quantitative
  use of research and operational satellite data in
  weather and climate analysis and prediction
  models
• Near-term Vision A weather and climate analysis
  and prediction community empowered to effectively
  assimilate increasing amounts of advanced
  satellite observations
• Long-term Vision An environmental analysis and
  prediction community empowered to effectively use
  the integrated observations of the GEOSS

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Goals Short/Medium Term

• Increase uses of current and future satellite
  data in Numerical Weather and Climate Analysis
  and Prediction models
• Develop the hardware/software systems needed to
  assimilate data from the advanced satellite
  sensors
• Advance the common NWP models and data
  assimilation infrastructure
• Develop common fast radiative transfer system
• Assess the impacts of data from advanced
  satellite sensors on weather and climate analysis
  and prediction
• Reduce the average time for operational
  implementations of new satellite technology from
  two years to one

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Goals Longer Term

• Provide the bridge for the integrated use of
  GEOSS data within numerical models
• Develop the tools for effective integration of
  GEOSS observations into environmental models
• Expand assimilation system to provide input to
  models of
• environmental hazards
• air and water quality and resources
• terrestrial, coastal, and marine ecosystems
• climate variability and change
• agricultural productivity
• energy resources
• human health
• biodiversity

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JCSDAs Role in Satellite Program Developments
Involvement in the end to end process
Definition of scientific and operational
requirements for new instruments CRTM forward
and adjoint model development Observation system
simulation experiments (OSSEs) Involvement in
instrument CALVAL Error characterization of
instrument observations Quality assurance and
forward model refinement (tuning) Data
assimilation and numerical model testing Analysis
of impact on analyses and forecasts Delivery and
transfer of improved forecast system to the
operational communities
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The Challenge Satellite Systems/Global
Measurements
GRACE
Aqua
Cloudsat
CALIPSO
TRMM
GIFTS
SSMIS
TOPEX
NPP
Landsat
Meteor/ SAGE
GOES-R
COSMIC/GPS
NOAA/POES
NPOESS
SeaWiFS
Aura
Jason
Terra
SORCE
ICESat
WindSAT
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Satellite Instrument Data Base
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NPOESS Satellite
CMIS
ATMS
CMIS- µwave imager VIIRS- vis/IR imager CrIS-
IR sounder ATMS- µwave sounder OMPS-
ozone GPSOS- GPS occultation ADCS- data
collection SESS- space environment APS- aerosol
polarimeter SARSAT - search rescue TSIS- solar
irradiance ERBS- Earth radiation budget ALT-
altimeter SS- survivability monitor
VIIRS
CrIS
OMPS
ERBS
The NPOESS spacecraft has the requirement to
operate in three different sun synchronous
orbits, 1330, 2130 and 1730 with different
configurations of fourteen different
environmental sensors that provide environmental
data records (EDRs) for space, ocean/water, land,
radiation clouds and atmospheric parameters. In
order to meet this requirement, the prime NPOESS
contractor, Northrop Grumman Space Technology, is
using their flight-qualified NPOESS T430
spacecraft. This spacecraft leverages extensive
experience on NASAs EOS Aqua and Aura programs
that integrated similar sensors as NPOESS. As
was required for EOS, the NPOESS T430 structure
is an optically and dynamically stable platform
specifically designed for earth observation
missions with complex sensor suites. In order to
manage engineering, design, and integration
risks, a single spacecraft bus for all three
orbits provides cost-effective support for
accelerated launch call-up and operation
requirement changes. In most cases, a sensor can
be easily deployed in a different orbit because
it will be placed in the same position on the any
spacecraft. There are ample resource margins for
the sensors, allowing for compensation due to
changes in sensor requirements and future planned
improvements. The spacecraft still has reserve
mass and power margin for the most stressing 1330
orbit, which has eleven sensors. The five panel
solar array, expandable to six, is one design,
providing power in the different orbits and
configurations.
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5-Order Magnitude Increase in Satellite Data
Over 10 Years

Daily Upper Air Observation Count
Satellite Instruments by Platform
NPOESS METEOP NOAA Windsat GOES DMSP
2003
Count
2002
Count (Millions)
1990
2010
2000
1990
2010
2010-250ch
Year
Year
Year
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Satellite Data used in NWP

• Quikscat ocean surface wind vectors
• AVHRR SST
• AVHRR vegetation fraction
• AVHRR surface type
• Multi-satellite snow cover
• Multi-satellite sea ice
• SBUV/2 ozone profile and total ozone
• Altimeter sea level observations (ocean data
  assimilation)
• Current Upgrade adds
• AIRS, MODIS Winds

• HIRS sounder radiances
• AMSU-A sounder radiances
• AMSU-B sounder radiances
• GOES sounder radiances
• GOES, Meteosat, GMS winds
• GOES precipitation rate
• SSM/I precipitation rates
• TRMM precipitation rates
• SSM/I ocean surface wind speeds
• ERS-2 ocean surface wind vectors

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JCSDA Road Map (2002 2010)
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JCSDA Road Map (2002 - 2010)
By 2010, a numerical weather prediction community
will be empowered to effectively assimilate
increasing amounts of advanced satellite
observations
The radiances can be assimilated under all
conditions with the state-of-the science NWP
models
Resources
NPOESS sensors ( CMIS, ATMS) GIFTS, GOES-R
OK
Required
Advanced JCSDA community-based radiative transfer
model, Advanced data thinning techniques
The CRTM include cloud, precipitation, scattering
The radiances from advanced sounders will be
used. Cloudy radiances will be tested under
rain-free atmospheres, more products (ozone,
water vapor winds)
AIRS, ATMS, CrIS, VIIRS, IASI, SSMIS,
AMSR,WINDSAT,CHAMP COSMIC,products assimilated
Science Advance
A beta version of JCSDA community-based radiative
transfer model (CRTM) transfer model will be
developed, including non-raining clouds, snow and
sea ice surface conditions
Improved JCSDA data assimilation science
The radiances of satellite sounding channels were
assimilated into EMC global model under only
clear atmospheric conditions. Some satellite
surface products (SST, GVI and snow cover, wind)
were used in EMC models
AMSU, HIRS, SSM/I, Quikscat, AVHRR, TMI, GOES
assimilated
Pre-JCSDA data assimilation science
Radiative transfer model, OPTRAN, ocean microwave
emissivity, microwave land emissivity model, and
GFS data assimilation system were developed
2002
2008
2009
2003
2010
2004
2007
2005
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Short Term Priorities 04/05

• SSMIS Collaborate with the SSMIS CALVAL Team to
  jointly help assess SSMIS data. Accelerate
  assimilation into operational model as
  appropriate
• MODIS MODIS AMV assessment and enhancement.
  Accelerate assimilation into operational model.
• AIRS Improved utilization of AIRS
• Reduce operational assimilation time penalty
  (Transmittance Upgrade)
• Improve data coverage of assimilated data.
  Improve spectral content in assimilated data.
• Improve QC using other satellite data (e.g.
  MODIS, AMSU)
• Investigate using cloudy scene radiances and
  cloud clearing options
• Improve RT Ozone estimates

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Short Term Priorities 05/06

• SSMIS Collaborate with the SSMIS CALVAL Team to
  jointly help assess SSMIS data. Accelerate
  assimilation into operational model as
  appropriate
• GPS GPS (CHAMP) assimilation and assessment.
  Accelerate GPS (COSMIC) assimilation into
  operational model.
• WINDSAT Assimilation and assessment.
• IASI Complete preparations for METOP/IASI
• Complete Community RTM transmittance preparation
  for IASI
• Assimilate synthetic IASI BUFR radiances in
  preparation for METOP.

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Major Accomplishments

• Common assimilation infrastructure at NOAA and
  NASA
• Community radiative transfer model
• Common NOAA/NASA land data assimilation system
• Interfaces between JCSDA models and external
  researchers
• Snow/sea ice emissivity model permits 300
  increase in sounding data usage over high
  latitudes improved polar forecasts
• MODIS winds, polar regions - improved forecasts
• Improved physically based SST analysis
• Advanced satellite data systems such as DMSP
  (SSMIS), CHAMP GPS data and EOS (MODIS Winds,
  Aqua AIRS, AMSR-E) being tested for
  implementation
• Impact studies of POES AMSU, Quikscat, GOES and
  EOS AIRS/MODIS with GMAO/NCEP data assimilation
  system

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JCSDA RECENT ADVANCES
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Figure 3. Latest optical path of gaseous
transmittance model performed at 19 HIRS channels
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Figure 4. Impact of sea ice and snow emissivity
models on the GFS 24 hr. fcst. at 850hPa. (1 Jan.
15 Feb. 2004) the pink curve shows theACC
with new snow and sea ice emissivity models
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Figure 7. Impact of MODIS AMVs on the operational
GFS forecast at 500hPa (60S - 90S). (10 Aug.
23 Sept. 2004) the pink (dashed) curve shows the
ACC with (without) MODIS AMVs
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AIRS Data Assimilation J. Le Marshall, J. Jung,
J. Derber, R. Treadon, S.J. Lord, M. Goldberg, W.
Wolf and H-S Liu, J. Joiner, and J
Woollen 1-27 January 2004 Used operational
GFS system as Control Used Operational GFS
system Plus Enhanced AIRS Processing as
Experimental System
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Figure 5.Spectral locations for 324 AIRS thinned
channel data distributed to NWP centers.
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Table 2 AIRS Data Usage per Six Hourly Analysis
Cycle
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Figure 1(b). 500hPa Z Anomaly Correlations for
the GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Southern hemisphere, January 2004
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Figure 3(b). 500hPa Z Anomaly Correlations for
the GFS with (Ops.AIRS) and without (Ops.) AIRS
data, Northern hemisphere, January 2004
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Assimilation of GPS RO observations at JCSDA

• Lidia Cucurull, John Derber, Russ Treadon, Martin
  Bohman, Jim Yeo

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Motivation

• To develop the total infrastructure (codes,
  scripts, etc.) necessary to monitor and
  assimilate radio-occultation (RO) observations by
  JCSDA partners and the wider community.
• Work schedule enables complete preparation of
  JCSDA data assimilation system in time for COSMIC
  launch (estimated Dec 2005 or Jan 2006).

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GPS - CHAMP/COSMIC
Non linear operator implemented in GSI 3D
Var. Error characterization well advanced CHAMP
observation tests ongoing, including study of
CHAMP use with complementary data. Study of
CHAMP/COSMIC logistics for RT application
underway
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GPS - CHAMP/COSMIC
Assessment of Refractivity vs Bending Angle
approaches Assessment of Local vs Non-Local
approaches
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CHAMP data flow to JCSDA
This system is currently under development by
UCAR JCSDA
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CHAMP NRT END TO END/ASSIM TEST March 2005
BUFR encoding software complete NCEP BUFR
software complete OSDPD agreement written Test
data tanks to be formed Prelim assim code
complete GFZ/DWD to be further engaged
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COSMIC data flow to Weather Centers
This system is currently under development by
UCAR, NESDIS, UKMO
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COSMIC END TO END/ASSIM TEST July 2005
BUFR encoding software complete NCEP BUFR
software complete OSDPD agreement written NCO
to be engaged/Test data tanks to be
formed Prelim assim code complete GFZ/DWD to be
kept engaged
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CY 2004 Activity

• Implementation of the local refractivity operator
  in the (new NCEP) Gridpoint Statistical
  Interpolation (GSI) analysis system.
• Improvement of the Forward Operator .
• Ability to ingest refractivity profiles in the
  system.
• Compute the innovation vector with CHAMP data
  (Forward Model).
• Tangent Linear and Adjoint codes (implemented and
  tested with the improved Forward Operator).
• Impact studies using single observation and
  single profile of refractivity.
• Impact studies of the assimilation of all
  profiles available at a given analysis time.
• Account for the different vertical resolution
  between observations and model.
• Implementation of QC checks in the code.

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6hr Window Profiles Assimilation

• Analysis Time 2002080812 (46 CHAMP profiles)
• Observations of refractivity rejected if
• (O-B)/error(O) gt 10
• Deviate more than 5 from background below 5 km
  (and if so, also remove observations below this
  altitude).

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Assimilation well behaved
1st iteration Bias 0.0021 rms 0.0133
2nd iteration Bias 0.0010 rms 0.0090

• The analysis fits the data better after each
  iteration
• Some data rejected at first, get into the system
  in later iterations.

3rd iteration Bias 0.0010 rms 0.0089
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Outlook for CY 2005

• (1) QC
• Test and implement additional QC checks (in lower
  troposphere, stratosphere, to account for
  superrefraction)
• (2) Error
• Better characterization of the refractivity
  (measurement) errors
• Examine representativeness error.
• Adjustment of the background error covariance
  matrix according to the results of the
  assimilation of profiles of refractivity.
• (3) Experiments
• Conduct a cycling experiment for one month period
  to assess the impact of the assimilation of the
  CHAMP RO profiles to get ready for COSMIC.
• (4) Forward Operators
• Implementation of the bending profile Forward
  Operator. (As the bending angle observations are
  less contaminated by the climatological guess
  field, a better performance in the assimilation
  system is expected.
• (5) Pass CHAMP data in COSMIC format from CDAAC
  through OSDPD to JSCDA/NCEP for assimilation
  studies.

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Note

• Preparations require community support
• Data access required early
• Staff stability

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Prologue

• JCSDA well positioned to exploit the COSMIC
  Program in terms of
• Assimilation science
• Modeling science.
• Computing power
• Generally the next decade of the meteorological
  satellite program promises to be every bit as
  exciting as the first, given the opportunities
  provided by new observations, modern data
  assimilation techniques, improving environmental
  modeling capacity and burgeoning computer power.
• The Joint Center will play a key role in
  enabling the use of advanced satellite data such
  as RO data, from both current and future
  advanced systems, for environmental modeling.
• USA Inc. and the Global Community will be a
  significant beneficiary from the Centers activity.

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