Satellite Ocean Data Assimilation at the Joint Center for Satellite Data Assimilation and NOAA

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The Joint Center for
Satellite Data Assimilation
Coastal Ocean Data
Assimilation Workshop
3 – 5 April 2007
Joint Center for Satellite
Data Assimilation
PARTNERS
NOAA/NCEP
Environmental
Modeling Center
NASA/Goddard
Global Modeling &
Assimilation Office
NOAA/OAR
NOAA/OAR
OfficeOffice
of Weather
of Weather
and Air
andQuality
Air Quality
US Navy
Oceanographer of the Navy,
Office of Naval Research (NRL)
US Air Force
NOAA/NESDIS
Center for
Satellite Applications and Research
AF Director of Weather
AF Weather Agency
JCSDA Mission
• Mission: Accelerate and improve the
quantitative use of research and
operational satellite data in weather,
climate and environmental analysis and
prediction models
The Challenge:
Satellite Systems/Global Measurements
GRACE
Aqua
TRMM
Cloudsat
CALIPSO
GIFTS
NPP
SSMIS
Landsat
NPOESS
TOPEX
COSMIC/GPS
Aura
Meteor/
SAGE
NPOESS
METEOP
NOAA
Windsat
GOES
DMSP
NOAA/
POES
GOES-R
SeaWiFS
Terra
Jason
SORCE
ICESat
WindSAT
5-Order Magnitude Increase in
Satellite Data Over 10 Years
JCSDA Data Assimilation Opportunities
Atmospheric Chemistry
Air Quality
ATMOSPHERE
• Global NWP
• Regional NWP
COUPLED
• Near-real time
• Climate
OCEAN
• Global near-real time
• Coastal near-real time
ECOSYSTEMS
SPACE
• Ionospheric DA
Priorities and Goals
RESEARCH AND DEVELOPMENT PRIORITIES
•
Improve radiative transfer models
•
Prepare for use of advanced instruments with enhanced capabilities
•
Advance techniques for assimilating cloud and precipitation observations
•
Advance techniques for assimilating land surface observations
•
Advance techniques for assimilating ocean observations
•
Advance techniques for assimilating atmospheric chemistry observations
GOALS
ƒ
Increase uses of current and future satellite data in Numerical Weather and Climate Analysis and Prediction
models
ƒ
Develop the hardware/software systems needed to assimilate data from the advanced satellite sensors
ƒ
Advance common numerical models and data assimilation infrastructure
ƒ
Develop a common fast radiative transfer system(CRTM)
ƒ
Assess impacts of data from advanced satellite sensors on weather, climate, and environmental analysis
and forecasts (OSEs, OSSEs)
ƒ
Reduce the average time for operational implementations of new satellite technology from two years to one
JCSDA Ocean Data Assimilation
Challenges
•
Satellites
–
–
–
Exploit satellite investment and ensure continuity
Prepare data streams, evaluate quality/impact
Current
•
–
Future
•
•
NPOESS, GOES-R, Aquarius, METOP, DMSP, Jason-2
Infrastructure
–
–
–
–
–
•
GOES, POES, METOP, DMSP, Jason
Optimize assimilation for efficiency, accuracy, and uncertainty
Expand ocean computational capacity
Develop ocean OSE capability
Develop ocean OSSE capability
Support IOOS national backbone for modeling and data assimilation
Applications
–
–
Maximize use of satellite ocean data
Atmospheric modeling
•
–
Coupled modeling
•
–
Climate & near-real time
Ocean modeling
•
–
–
Global & regional
Near-real time global & regional
Coastal modeling
Ecosystem modeling
JCSDA: Ocean Data Assimilation
Key contributions sought in JCSDA collaborations:
•
Estimation of observational error characteristics for satellite data used in ocean state
estimation (surface altimetry, microwave and IR SST retrievals), specifically,
covariances, biases, correlated errors, and errors of representativeness; e.g.:
–
–
–
•
altimeter significant wave height corrections from buoy match-ups;
altimeter sea-surface height corrections for removal of inverse barometer and barotropic
signals;
identification of diurnal warming and skin affects in the satellite SST data.
Assimilating satellite data products to improve forecasts of the ocean mesoscale or
seasonal climate anomalies; e.g.,
–
–
Improving the current methods for assimilating altimetry, possibly identifying a ‘communitybased approach’;
Direct radiance assimilation
•
Expanding the current operational system capabilities to include preparation for the
assimilation of remotely-sensed surface salinity, assimilation of sea-ice, timevarying ocean color, GRACE, GPS data, or other satellite observations.
•
Observing system experiments to help define the requirements for remotely sensed
surface salinity and accuracy requirements for improved sea surface temperature;
•
Validation of ocean assimilation products and ocean forecasts with satellite products.
JCSDA 2006 Ocean Projects
•
WindSat Assimilation
–
–
•
SST Assimilation
–
–
–
•
–
Transition assimilation of altimeter SWH observations for the Navy's global WW3 forecast model.
Perform validation studies to test and improve the assimilation of Altimetric SSH into real-time Navy ocean prediction systems.
Specifically, the systems includes ALPS, MODAS, NCODA, NLOM, NCOM and HYCOM.
Integrate the NPOESS altimeter (ALT) sensor observations into the automated processing system at NAVOCEANO, perform
quality control on the data, ensure that corrections for atmosphere and electron content are accurate, and provide appropriate
spatial and temporal covariance functions for numerical model assimilation systems. Use of the NPOESS ALT will allow
nowcast and prediction of the ocean mesoscale currents and impact on temperature and salinity through assimilation into
numerical ocean models both globally and regionally nested.
Sea Ice
–
•
Continue to improve physical SST retrievals based on Variational Technique; improve Real-Time Global SST analysis; use
additional instruments in physical retrieval algorithms as appropriate
Perform validation studies to test and improve the assimilation of satellite derived SST into real-time Navy ocean prediction
systems. Specifically, the systems includes MODAS, NCODA, NLOM, NCOM and HYCOM. The research supports the
assimilation algorithms
Incorporate the NPOESS observations that affect the upper ocean layer into operational systems. These observations include
heat fluxes and surface temperature from infrared (IR) and passive microwave.
Altimetry
–
–
•
Evaluate WindSat wind vectors/improve algorithms relative to other marine winds, and their impact on global forecasts
Develop forward models of ocean surface emission and radiative transfer; develop physically-based retrieval algorithms for wind
vector, SST, integrated water vapor and cloud liquid water
Perform validation tests of assimilation of SSMI ice concentration data into the new PIPS 3.0 system based on the Los Alamos
CICE model.
Assimilation Methodology
–
–
Develop and test algorithms for a 3-D MVOI methodology for ocean data assimilation. This research is based on the NCODA
system and is coordinated with atmospheric data assimilation algorithm development for a planned upgrade to a 3D-VAR
(NAVDAS).
Develop and test algorithms for 4DVAR assimilation of ocean data (SSH, SST, in-situ T, S, U, and V) into ocean models using
model adjoints .
JCSDA Grants Program
•
Special attention paid to clearly identified paths from research to
operations and coordination with PIs working in similar research areas
– Particularly useful to have an identified JCSDA partner / collaborator /
mentor / contact to integrate the research output into the operational
program
•
Supports projects with longer-term goals, i.e. potential evaluation for
operations within 3 – 5 years
•
Annual Announcements of Opportunity ~ July/August
– Average grant: $100K/Yr for 1 to 3 years
– 21 grants in place
Education and Outreach
• JCSDA recognizes the scarcity of trained
and qualified data assimilation scientists
• Newsletter
• Web Page
• Seminar Series
• Workshops
JCSDA
RECENT ADVANCES
Recent JCSDA Research
• Improved Physical Retrieval Technique for SST (Xu Li and John Derber, NCEP/EMC)
• Analyze SST by assimilating satellite radiances directly with GSI
– 6-hourly skin temperature analysis
• Aerosol Effect
– Radiance increment dependency on Navy aerosol optical depth
• Incorporation of oceanic components in GSI
– Diurnal warming and subsub-layer cooling model (in development)
– Oceanic model in GFS and coupling?
• AVHRR Radiance Bias Correction (Andrew Harris, Jonathan Mittaz, CICS/UMD)
• Pursue physically-based methodologies to provide:
–Techniques to identify instrument calibration and characterization
characterization postpost-launch
–Improved AVHRR SST retrieval capability (inc. diurnal)
–Feed back results to improve forward modeling
• Bayesian cloud detection a promising method for assigning quantitative errors to
individual pixels
• Improving analysis of tropical upper ocean conditions for forecasting (Jim Carton , UMD)
• Forecast bias correction implemented in NCEP’s GODAS
• Use of Wavenumber Spectrum of Sea Surface Height for constructing error models in
ocean data assimilation (Alexey Kaplan, LDEO)
Recent JCSDA Research
• Collaborations on SST:
– Use of aerosol data - collaboration with Clark Weaver (and A. da
Silva) on aerosol products
– Detection and correction of aerosol contamination in infrared
SST retrievals (Jim Cummings, NRL/Monterey; Andrew Harris,
NESDIS)
– Inclusion of emissivity/reflectance model in forward model in
NCEP radiance assimilation (Nalli)
– Inclusion of ocean mixed-layer model (Li, Harris, Rienecker)
• Collaborations on Assimilation Methodology:
–
–
–
Bias estimation (Carton, Keppenne)
Methods to assimilate altimetry - noise models (Kaplan, GMAO)
Ensemble generation (Kaplan, GMAO)
NOAA
Operational
Ocean Data Assimilation
Priorities
• Assimilating Data Streams
–
Exploiting existing ocean data streams
•
SST:
–
–
•
•
•
•
•
–
SSH: GFO, Jason-1
Ocean Vector Winds: QuikScat, WindSat
Sea Ice: AMSR-E, WindSat, QuikScat, SAR
In-situ: Argo floats, oceanographic monitoring stations (e.g., ADCP)
Ships of opportunity
Preparing for continuity operational data streams
•
•
•
•
•
–
Infrared: GOES, MODIS
Microwave: TMI, WindSat, AMSR-E
SST: MetOp, NPP, GOES-R, NPOESS
SSH: Jason-2
Ocean Vector Winds: ASCAT
Sea Ice: ASCAT
In-situ:
Preparing for new operational data streams
•
•
•
•
•
•
HF radar
SSS: SMOS, Aquarius
Ocean color: NPP, GOES-R, NPOESS
Ocean vector winds: Synthetic Aperture Radar (SAR); ASAR, ALOS, Radarsat
Sea Ice: Synthetic Aperture Radar (SAR); ASAR, ALOS, Radarsat
IOOS data
Priorities
• Modeling Assimilation
– Techniques / methodologies
• Community-based approaches
– Application
• Coupled/active ocean modeling for atmospheric modeling
– Hurricane WRF, GFS
• Ocean modeling
– RTOFS, GODAS, Wavewatch-3
• Estuary, Great Lakes, and Coastal modeling
– Regional
– High-resolution
• Climate modeling
– Seasonal-interannual predictions
– Decadal projections
• Ecosystem Modeling
– Data assimilation via coupling with physical/hydrodynamic models
– Direct assimilation
Satellite Data used in NOAA NWP
• 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
• Quikscat ocean surface
wind vectors
• AVHRR SST
• AVHRR vegetation fraction
• AVHRR surface type
• Multi-satellite snow cover
• Multi-satellite sea ice
(DMSP, AMSR-E)
• SBUV/2 ozone profile and
total ozone
• Altimeter sea level
observations (ocean data
assimilation)
• AIRS
• Current Upgrade adds;
MODIS Winds…
NOAA Ocean DA Horizon
ATMOSPHERE
COUPLED
• NearNear-real time (global, regional, hurricane)
• Expand data streams & parameters
• SST, altimetry, ocean vector winds, sea ice
• Active ocean
• 2-way coupling
• Climate
• Ocean color
• Salinity
• Sea ice
OCEAN
• Global nearnear-real time
• Expand operationally assimilated data streams & parameters
• SST, altimetry, ocean vector winds, sea ice
• Spectral assimilation for waves (Wavewatch-3)
• Enhance assimilation into sea-ice models
• Salinity
• Coastal nearnear-real time
• Implement research & operational assimilation
• Expand data streams & parameters
• SST, altimetry, ocean vector winds, sea ice
• Explore synthetic aperture radar data assimilation
ECOSYSTEMS
• Implement research & operational assimilation
• Harmful Algal Blooms (HABs)
• Ocean color / primary productivity
• Storm surge
• Particle trajectories
Observations
NASA-NOAA-DOD
JCSDA
Satellite
(AVHRR, JASON, QuikSCAT)
AMSR, GOES,
AIRS, JASON, WindSat,
MODIS
Advanced
ODA Techniques
In situ
(ARGO, Buoys, Ships)
Data Cutoff
CFS: 2 week data cutoff
RTOFS: 24 hour data cutoff
OCEAN DATA ASSIMILATION
CLIMATE FORECAST
CFS-GODAS
NCO/ODA
EMC
NOPP-JPL (ECCO)
OCEAN FORECAST
RT-OFS-GODAE
Shared history,
coding, and data
processing
NOPP
EMC
MOM-3 Æ MOM-4 Æ HOME OPNL OCEAN FORECASTS
Climate Forecast System
http://cfs.ncep.noaa.gov/
HYCOM Æ HOME
Real-Time Ocean Forecast System
http://polar.ncep.noaa.gov/ofs/
Seasonal to Interannual Prediction at NCEP
MOMv3
GODAS
3DVAR
XBT
TAO etc
Argo
Salinity (syn.)
(TOPEX/Jason-1)
Climate
Forecast
System
(CFS)
GFS
Reanalysis-2
3DVAR
T62L28
update of the
NCEP-NCAR R1
Seasonal to Interannual Forecasting at NCEP
Altimeter
SST
Argo
XBT
TAO
Global Ocean
Data Assimilation
System (GODAS)
Ocean Initial Conditions
Coupled Ocean
Atmosphere Forecast
System (CFS03)
IRI
SST Anomaly
Stress
Seasonal Forecasts
for North America
with Climate
Atmosphere GCM
CCA, OCN
MR, ENSO
Surface Temperature
& Rainfall Anomalies
Heat Fluxes
IRI
E-P
Scatterometer
CCA, CA
Markov
Official SST Forecast
Forecasters
Official Probabilistic
Surface Temperature
& Rainfall Forecasts
NOAA
Global Ocean Data Assimilation System (GODAS) /
Climate Forecast System (CFS)
Planned
Potential
GOAL
NOW
NOW
OBJECTIVE:
Maximized satellite
data assimilation into
operational models,
with minimized errors
and uncertainty
More accurate environmental forecasts through optimal use of Satellite Data
DRAFT
GODAS (seasonal-interannual ocean: MOM)
CFS (seasonal-interannual, coupled ocean-atm: MOM)
NPP VIIRS
Ocean Color
Ocean color
SSH
NPOESS VIIRS
Ocean color
Add bio-physical
feedback mechanism
ERS-2 SSH
GFO SSH
Jason-1SSH
Sea Ice
Add dynamic topography
with heat content signal
Jason-2 SSH
Climatological
sea ice
(MOM4)
SST
Interactive sea
ice field
Add sea ice; improve
temporal & spatial
resolution
Assimilated
sea ice data
Non-NOAA SST (MODIS, AATSR, MTSAT, MSG, InSat, FY2C, …)
Increased coverage
Microwave SST
(AMSR-E, WindSat, TMI)
AVHRR
AVHRR
Increased coverage;
confirmation
Increased spatial
resolution;
GOES continuity
GOES-R ABI SST
OSSE/op preps/OSE
GOES-12/13 Imager SST
op preps/OSE
GOES-O Imager SST
op preps/OSE
NPP VIIRS SST
OSSE/op preps/OSE
MetOp-1
AVHRR
SST OSE
NOAA-18
AVHRR
SST OSE
Now
2007
Increased temporal
resolution;
GOES SST continuity
GOES-P Imager SST
op preps/OSE
NPOESS VIIRS SST
Op preps/OSE
MetOp-B
MetOp-2
AVHRR SST
prep/OSE
AVHRR replacement;
VIIRS SST continuity
MetOp-C
MetOp-3
AVHRR SST
prep/OSE
NOAA N’
AVHRR
SST prep/OSE
2008
2009
AVHRR SST continuity;
Increased resolution
AVHRR SST continuity
2010
2011
2012
2013
2014
2015
NCEP
Real-time Ocean Forecast System (RTOFS)
Product:
Data:
Data:
SST
SST
SSH
SSH
TT &S
&S
(AVHRR,GOES,
(AVHRR,GOES,
ARGO,
ARGO, JASON,
JASON,
Buoys,
Buoys, ships)
ships)
Data Assimilation:
Daily update of
T, S & SSH
(3DVAR)
Now-cast
5-day Forecast
Temperature
Salinity
Currents
Sea surface
Elevation
Forcing:
Air-sea fluxes
(GDAS & GFS)
River outflow
(USGS, RIVDIS)
Tide
(TPX.03)
For open boundaries:
T,S & transport climatology
(historical data,
MDT [Rio5])
Service:
Ocean Dynamical Model
Hybrid layer model
(HYCOM)
Initial & Boundary
Data for Regional Ocean
and Atmospheric Models;
Support:
Marine Safety,
Management of Hazards,
Ecosystems;
Exploration & Exploitation
NOAA
Real-Time Ocean Forecast System (RTOFS)
Planned
Potential
NOW
NOW
GOAL
More accurate environmental forecasts through optimal use of Satellite Data
RTOFS (near-real time: HYCOM)
SAR sea ice
Sea Ice
(Envisat ASAR)
SSM/I
SSM/I
Active radar sea ice
(scatterometer)
Aqua AMSR-E
sea ice via
auto MW ice
product
SSH
DRAFT
OBJECTIVE:
Maximized satellite
data assimilation into
operational models,
with minimized errors
and uncertainty
Improved sea ice
temporal coverage
& spatial resolution:
augment SSMI
WindSat sea
ice
ERS-2 SSH
GFO SSH
Jason-1
SSH
Add dynamic topography
with heat content signal
Jason-2 SSH
Winds
SSM/I
SSM/I
MetOp-1
ASCAT winds
OSE
SST
MetOp-2
ASCAT winds
prep/OSE
MetOp-3
ASCAT winds
prep/OSE
Non-NOAA SST (MODIS, AATSR, MTSAT, MSG, InSat, FY2C, …)
AVHRR
AVHRR
GOES**
GOES**
Increased coverage
Microwave SST
(AMSR-E, WindSat, TMI)
Increased coverage;
verification
** Limited
Increased spatial
resolution;
GOES continuity
GOES-R ABI SST
OSSE/op preps/OSE
GOES-12/13 Imager SST
op preps/OSE
GOES-O Imager SST
op preps/OSE
2007
NPOESS VIIRS SST
Op preps/OSE
MetOp-B
MetOp-2
AVHRR SST
prep/OSE
MetOp-1
MetOp-A
AVHRR
SST OSE
Now
Increased temporal
resolution;
GOES SST continuity
GOES-P Imager SST
op preps/OSE
NPP VIIRS SST
OSSE/op preps/OSE
NOAA-18
AVHRR
SST OSE
AVHRR replacement;
VIIRS SST continuity
MetOp-C
MetOp-3
AVHRR SST
prep/OSE
NOAA N’
AVHRR
SST prep/OSE
2008
2009
Operational ocean
vector winds via
regional atm forcing model;
scatterometry continuity
AVHRR SST continuity;
Increased resolution
AVHRR SST continuity
2010
2011
2012
2013
2014
2015
NOAA
WaveWatch-III
Planned
Potential
OBJECTIVE:
GOAL
NOW
NOW
More accurate environmental forecasts through optimal use of Satellite Data
Maximized satellite
data assimilation into
operational models,
with minimized errors
and uncertainty
DRAFT
WW-III (near-real time global waves)
Evolve altimetry
assimilation
methodology
SAR
Assimilate altimetry
data based on
spectral content
Envisat ASAR
swell spectral
data
Sea Ice
Active radar sea ice
(scatterometer)
SSM/I
SSM/I
Aqua AMSR-E
sea ice via
auto MW ice
product
SSH
Increase information
content
Improved sea ice
temporal coverage
& spatial resolution:
augment SSMI
WindSat sea
ice
Envisat altimeter
significant
wave height
ERS-2
ERS-2
GFO
significant
wave height
Jason-1
significant
wave height
Now
2007
Jason-2
significant
wave height
2008
Increase temporal
& spatial resolution
2009
2010
2011
2012
2013
2014
2015
Near-real-time Coupled Modeling
•
HURRICANE FORECASTING
– For 2007 hurricane season, NOAA intends to implement a coupled
forecasting system:
• Global Forecast System (GFS); global atmospheric model
• Hurricane – Weather and Research Forecast (HWRF) model; regional
atmospheric model
• Real-time Ocean Forecast System (RTOFS); Atlantic regional ocean
model
• WaveWatch3; global surface wave model
– Planned operational computing system upgrade (FY2007) to provide
sufficient computing to run fully-coupled HWRF and wave models.
•
FY09-13
– Improved data assimilation of altimetry for sea surface height
– Critical to the determination of the energy available in the upper ocean
• Given appropriate atmospheric conditions, the intensification of a
hurricane is driven largely by the upper-ocean available energy
Sea Surface Temperature 8/28/05
Hurricane
Katrina
Tropical Cyclone Heat Potential (TCHP) 8/28/05
Goni/AOML
(Planned for 2007)
Data Assimilation Gaps
•
Optimal exploitation of existing data streams
–
Unused data streams:
•
•
•
•
SST (GOES, microwave)
Sea ice (scatterometry, synthetic aperture radar, passive polarimetry)
Ocean vector winds (passive polarimetry)
Ocean color
•
Ocean/coastal assimilation methodology development
•
Implementation capacity
–
–
Quadrupled NOAA operational satellite ocean parameters
Ocean data assimilation computational capacity
•
•
•
Continuity of NOAA operational data streams
–
–
–
•
Research and development
Operational
SSH
Ocean vector winds (scatterometry)
Synthetic aperture radar (sea ice)
Coastal and ecosystem operational data assimilation
NOAA IOOS PROGRAM:
Integrate Data
Problem
Need
Global climate system
not well understood
• Characterize the
state of the global
climate system and its
variability
Coastal populations at
risk, including coastal
hazards and coastal
development and
urbanization
• Improved models
(e.g., coastal
inundation, hurricane
intensity, and harmful
algal bloom model)
Ocean, coastal, and
Great Lakes ecosystems
at risk, including the
hydrological and
biogeochemical cycles,
and ecosystem health and
productivity
• Improved ecosystem
assessments and
models
• Updated
management
approaches
• Improved access to
data, and scientific
information
Core Variables
Temperature
Salinity
Sea Level
Surface currents
Ocean color
Bathymetry
Surface waves
Ice distribution
Integration
Contaminants
Long-term data
Dissolved nutrients
series,
coordinated
Fish species
in space and time
Fish abundance
Zooplankton species
Optical properties
Heat flux
Bottom character
Pathogens
Dissolved O2
Phytoplankton species
Zooplankton abundance
Decision
Tools
Hurricane
Intensity
Model
Coastal
Inundation
Model
Harmful
Algal Bloom
Model
Integrated
Ecosystem
Assessment
NOAA IOOS PROGRAM:
Decision Tools – Integrated Core Variables for Models
CORE
VARIABLES
NOAA
MODELS
Current State
MODELING IMPROVEMENTS
(future state)
• Temperature
Hurricane
Intensity
Model
• Non real-time and
interpolated
temperature data
used to inform model
• Integration of real-time,
temperature = increased
accuracy of hurricane intensity
predictions
Process Flow
Evaluate NOAA models
that impact highestpriority problems
Integrate variables needed to
achieve benchmarked
improvements
• Sea Level
Coastal
Inundation
Model
• Sea level data
(various sources and • Expedited development of
coastal inundation forecasts for
formats) integrated on
Southeast and Gulf
site-by-site basis for
use in model
Quantify progress toward defined
modeling improvements
• Surface
currents
• Ocean Color
• Salinity
Identify additional source(s) of
error within model
Identify remaining IOOS
core variables needed
to reduce error
Select next set of priority core
variables based on impacts to
NOAA products
None?
•
•
•
•
Temperature
Salinity
Ocean Color
Surface
currents
• Sea level
Harmful Algal
Bloom Model
Integrated
Ecosystem
Assessment
• Improved bloom trajectory
• Wind data and marine
forecast
forecasts used as
•
Enable development of
proxy to determine
national HAB forecast with
currents
integrated currents
• Assess current conditions
• Forecast ecological health
• NOAA compiles and
based on existing management
integrates suite of
strategies
data required for each
assessment
• Evaluate impacts of alternate
management strategies
NOAA IOOS Program:
Data Integration Framework – Initial Operating Capability
Months 0-12
Month 18
Integration of 5 Core Variables
Integrated variable
ingest for following
products
Month 24
Month 36
Test & Evaluation
Benchmarked product
improvements for
operational use
NOAA 5 Core Variables
Temperature
Salinity
Sea Level
Currents
Color
Hurricane
Intensity Model
Hurricane
Intensity Model
Enhanced decision
support through:
PRIORITY 1
Systems Engineer
Standards
PLATFORM
Data Distribution
NOAA Ship Synoptic
Archive
NCDC
NOAA Ship Archive
NOAA Ships
ARGO Delayed data
ARGO Profiling
GDAC
Tropical Moored Buoys
Coastal
Inundation Model
Weather Buoys
NDBC
Drifting Buoys
NCDDC
SWMP
NEERS CDMO
VOS (xbt)
OAR
CREIOS
NOAA
IOOS Integrated Data Framework
Single Sat. Pass Data
Satellites
AOML
NMFS
NWLON
Multiple Sat. Pass Data
PACIFIC I. FSC
OSDPP
Salinity
Sea Level
Currents
• Integrated
information services
for NOAA programs
• Identify observation
gaps
CO-OPS
CLASS
COAST WATCH
• Test & Evaluation
Harmful Algal
Bloom Model
Temperature
Coastal
Inundation Model
• Product
Enhancement
NODC
DART
C-MAN
SWIM
Systems
Engineering:
Color
Integrated
Ecosystem
Assessment
• Verification &
Validation
Harmful Algal
Bloom Model
• Validated enhanced
data products
• NOAA’s Data
Integration
Framework
Integrated
Ecosystem
Assessment
NOAA
MISSION OBJECTIVES
External sources of 5 Core Variables
(consistent with NOAA standards)
Summary
• Oceanic satellite data assimilation is a core JCSDA mandate
• JCSDA’s ocean component is poised for growth
• Infrastructure issues need to be addressed
• computational resources
• implementation of operational ocean/coastal and coupled models
• funding wedge to support the assimilation of expanding observations
• Work in progress
• funded research and development
• assimilation techniques
• operational satellite data assimilation implementation
• SST
• altimetry
• sea ice
• ocean vector winds
• Notable opportunities exist, in particular for coastal data assimilation.
BACKUP SLIDES
Joint Center for Satellite Data Assimilation
Global Forecast System (GFS)
Planned
Potential
NOW
NOW
Currently Resourced
GOAL
Not Resourced
More accurate environmental forecasts through optimal use of Satellite Data
OBJECTIVE:
Maximized satellite
data assimilation into
operational models,
with minimized errors
and uncertainty
GFS (atmosphere model, ocean component)
DRAFT
Add active ocean; 1-D
mixed layer on atm
grid
Sea Ice
Active radar sea ice
(scatterometer)
Aqua AMSR-E
sea ice via
auto MW ice
product
SSM/I
SSM/I
Wind
SST
Improved sea ice
temporal coverage
& spatial resolution:
augment SSMI
WindSat sea
ice
MetOp-A
ASCAT winds
OSE
QuikScat
QuikScat
SSM/I
SSM/I
Initial atm-ocean
coupling
MetOp-B
ASCAT winds
prep/OSE
MetOp-C
ASCAT winds
prep/OSE
Non-NOAA SST (MODIS, AATSR, MTSAT, MSG, InSat, FY2C, …)
Increased coverage
Microwave SST
(AMSR-E, WindSat, TMI)
AVHRR
AVHRR
Increased coverage;
confirmation
Increased spatial
resolution;
GOES SST continuity
GOES-R ABI SST
OSSE/op preps/OSE
GOES-12/13 Imager SST
op preps/OSE
GOES-O Imager SST
op preps/OSE
2007
NPOESS VIIRS SST
Op preps/OSE
MetOp-B
AVHRR SST
prep/OSE
MetOp-A
AVHRR
SST OSE
Now
Increased temporal
resolution;
GOES SST continuity
GOES-P Imager SST
op preps/OSE
NPP VIIRS SST
OSSE/op preps/OSE
NOAA-18
AVHRR
SST OSE
AVHRR replacement;
VIIRS SST continuity
MetOp-C
AVHRR SST
prep/OSE
NOAA N’
AVHRR
SST prep/OSE
2008
2009
Operational ocean
vector winds;
scatterometry continuity
AVHRR SST continuity;
Increased resolution
AVHRR SST continuity
2010
2011
2012
2013
2014
2015
Global Forecast System (GFS)
•
Now
–
SST:
•
•
Uses weekly NCEP SST analysis (“Reynolds” SST)
Uses data in Gridpoint Statistical Interpolation (GSI) system
–
–
–
Sea ice:
•
–
Assimilates QuikScat scatterometry ocean vector winds
Planned
–
–
Adding active ocean: ~ Sep 06 (1-D mixed-layer model on atmospheric grid)
SST
•
–
Adding MetOp AVHRR 4km data: FY07
Sea ice
•
•
Uses automated microwave sea ice product based on SSMI data
Ocean winds
•
•
AVHRR
HIRS
Adding AMSR-E to automated microwave product: ~ Jun 07
Potential
–
SST
•
•
•
•
–
Sea ice
•
•
–
GOES Imager SST data (already in GSI)
International geostationary SST data: MTSAT, MSG, InSat, FY2C
Polar-orbiting SST data: Envisat AATSR, MODIS
Microwave data: AMSR-E, Windsat, TMI
Passive: Windsat
Active: QuikScat, ASCAT, Envisat ASAR), JAXA ALOS
Ocean Winds
•
•
MetOp ASCAT scatterometry ocean vector winds
Windsat passive polarimetry ocean vector winds
Joint Center for Satellite Data Assimilation
Regional Forecast System
Planned
Potential
NOW
NOW
Currently Resourced
GOAL
Not Resourced
More accurate environmental forecasts through optimal use of Satellite Data
OBJECTIVE:
Maximized satellite
data assimilation into
operational models,
with minimized errors
and uncertainty
WRF (atmosphere model, ocean component)
SAR sea ice
(Envisat ASAR)
Active radar sea ice
GIS layer
(scatterometer)
Sea Ice
Aqua AMSR-E
sea ice via
auto MW ice
product
SSM/I
SSM/I
Wind
SST
Improved sea ice
temporal coverage
& spatial resolution:
augment SSMI
WindSat sea
ice (passive
polarimetry)
MetOp-1
ASCAT winds
OSE
QuikScat
QuikScat
SSM/I
SSM/I
DRAFT
MetOp-2
ASCAT winds
prep/OSE
MetOp-3
ASCAT winds
prep/OSE
Non-NOAA SST (MODIS, AATSR, MTSAT, MSG, InSat, FY2C, …)
Increased coverage
Microwave SST
(AMSR-E, WindSat, TMI)
AVHRR
AVHRR
GOES**
GOES**
Increased coverage;
confirmation
GOES-O Imager SST
op preps/OSE
2007
NPOESS VIIRS SST
Op preps/OSE
MetOp-B
MetOp-2
AVHRR SST
prep/OSE
MetOp-1
AVHRR
SST OSE
NOAA-18
AVHRR
SST OSE
Increased temporal
resolution;
GOES SST continuity
GOES-P Imager SST
op preps/OSE
NPP VIIRS SST
OSSE/op preps/OSE
Now
Increased spatial
resolution;
GOES SST continuity
GOES-R ABI SST
OSSE/op preps/OSE
**Limited
GOES-12/13 Imager SST
op preps/OSE
AVHRR replacement;
VIIRS SST continuity
MetOp-C
MetOp-3
AVHRR SST
prep/OSE
NOAA N’
AVHRR
SST prep/OSE
2008
2009
Operational ocean
vector winds;
scatterometry continuity
AVHRR SST continuity;
Increased resolution
AVHRR SST continuity
2010
2011
2012
2013
2014
2015
•
Regional / Mesoscale
Now: Weather Research and Forecast (WRF) model
–
SST
•
–
Sea ice
•
–
WRF uses NCEP automated microwave sea ice product
Ocean winds
•
–
Daily SST analysis (NCEP Marine Modeling Branch): AVHRR + GOES for NW Atlantic
WRF assimilates SSMI (wind speed only), QuikScat (vector winds), ERS (when data useable)
Short-Range Ensemble Forecast (SREF)
•
•
Regional Spectral Model (RSM) ???
Eta North American model
–
–
•
Planned
–
SST
•
–
Changing from NCEP weekly analysis (Reynolds SST) to Pathfinder SST for relaxation climatology?
Sea ice
•
•
•
–
SST ??
Uses the NESDIS Interactive Multi-sensor Snow/ice (IMS) product, incorporates a National Ice Center (NIC) analysis from multiple
sensors
Assimilates SSMI (wind speed only)
Adding AMSR-E to automated microwave product: ~ Jun 07
NIC will provide QuikScat GIS layer: ~ Dec 06
–
Ocean winds: WindSat, MetOp ASCAT
–
SST
Potential
•
•
•
•
MetOp AVHRR 1 km SST data
MODIS 1 km SST data (preparation for NPP/NPOESS VIIRS data)
Envisat AATSR
Geostationary (GOES): need global retrievals, requiring international data; MTSAT, MSG, InSat, FY2C
–
–
•
–
Microwave: Considering AMSR-E; potentially Windsat
Sea ice
•
•
•
–
Need smoother data to reduce jumps between subsequent observations
Concerns about GOES SST calibration
Desire Envisat ASAR global monitoring 1km (matches model resolution)
JAXA ALOS L-band synthetic aperture radar data; Algorithms and impact assessment required
Windsat (passive polarimetric sea ice algorithms needed)
Ocean winds
•
Envisat ASAR 1-km winds
Global Ocean Data Assimilation System (GODAS)
Coupled Forecast System (CFS)
•
GODAS provides unattached assimilation analysis for the Coupled Forecast System (CFS)
–
–
•
Now
–
Approximately 2 weeks behind present state
Ocean model (MOM3) forced by Reanalysis-2 atmosphere
•
•
SST
•
•
Relaxes (5-days) model values to NCEP weekly analysis (Reynolds SST), which incorporates AVHRR data
Planned
–
SST
–
SSH
•
•
•
–
MetOp AVHRR (4 km resolution) in continuity of AVHRR data stream
Jason-1 altimetry data: ~ Sep 06
Jason-2 altimetry data: ~ FY08
Sea ice
•
•
•
•
Update to MOM4 tied to CFS update
Prototype MOM4 expected soon
GODAS(MOM4) will include climatological sea ice
Interactive ice field: ~ FY09
Assimilated sea ice data: > FY09
Potential
–
SST
•
•
•
•
–
Sea ice
•
•
–
Passive: SSMI, AMSR-E, Windsat
Active: QuikScat, MetOp ASCAT, Envisat ASAR, JAXA ALOS
SSH
•
•
–
GOES Imager SST data (already in GSI)
International geostationary SST data: MTSAT, MSG, InSat, FY2C
Polar-orbiting SST data: Envisat AATSR, MODIS
Microwave data: AMSR-E, Windsat, TMI
GFO data currently available
ESA ERS-2 data currently available
Ocean color
•
SeaWiFS, MODIS, NPP/NPOESS VIIRS for biophysical feedback
Real-Time Ocean Forecast System (RTOFS)
•
Now
–
–
Forced by WRF regional model, thereby incorporating the WRF data assimilation
SST
•
•
AVHRR
GOES
–
–
–
–
Sea ice
–
Ocean winds
•
•
•
Uses automated microwave sea ice product based on SSMI data
SSMI (wind speed only) via NAM forcing
Planned
–
–
Global RTOFS (FY07); Pacific Coast RTOFS (mid FY08)
SST
•
•
–
–
–
MetOp AVHRR (4km resolution) in continuity of AVHRR data stream
Assimilation of MODIS SST data planned, but not a priority
– Need instrument statistics
Sea ice
Adding AMSR-E to automated microwave product: ~ Jun 07
SSH
•
•
•
Only using for NW Atlantic
Can be turned on everywhere
Do not understand biases well; needs/wants NESDIS partner to help
Jason-1 and GFO data: imminent implementation; FY06 Q4
Jason-2: ~ FY08
Potential
–
SST
–
Sea ice
•
•
•
–
Desire Envisat AATSR data
Passive: Windsat
Active: QuikScat, MetOp ASCAT, Envisat ASAR, JAXA ALOS
Ocean winds
•
•
•
Supports best estimate in nowcasts; useful for hindcasts
QuikScat, MetOp ASCAT scatterometry ocean vector winds
WindSat passive polarimetry ocean vector winds
–
SSH
–
Ocean color
•
•
•
•
•
ESA ERS-2 data currently available
Facilitates resolution of mesoscale features
Provides strong signature of mesoscale motion
Data on water clarity would help improve other model parameter estimations
SeaWiFS, MODIS, NPP/NPOESS VIIRS
WAVEWATCH III
•
Now
–
SSH
•
–
Sea ice
•
•
Uses automated microwave sea ice product based on SSMI data
Planned
–
SSH
•
•
–
Jason-1 data assimilation implementation imminent: ~ Jul 06
Jason-2 data assimilation: ~ FY08
Sea ice
•
•
ESA ERS-2 altimetry data
Adding AMSR-E to automated microwave product: ~ Jun 07
Potential
–
SSH
•
•
•
GFO altimetry data (currently received, but not processed)
Envisat altimeter (data request pending at ESA)
Envisat ASAR data would provide spectral data for swell groups
–
•
JAXA ALOS L-band synthetic aperture radar data
–
–
Need ESA to release this data to NCEP
Algorithms and impact assessment required
Sea ice
•
•
Passive: Windsat
Active: QuikScat, MetOp ASCAT, Envisat ASAR, JAXA ALOS

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