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GOVST-V Capital Hotel, Beijing, China, 13-17 Oct 2014 Lecce, Italy, Feb 4-7, 2012 Links between OSEval and COSS Villy Kourafalou Univ. of Miami/RSMAS OSEval-TT
GOVST-V Capital Hotel, Beijing, China, 13-17 Oct 2014 Lecce,
Italy, Feb 4-7, 2012 Links between OSEval and COSS Villy Kourafalou
Univ. of Miami/RSMAS OSEval-TT Oct. 2014 COSS Mission Goal: Advance
science in support of sustainable multidisciplinary downscaling and
forecasting activities in the world coastal oceans. COSS Strategic
goal: Help achieve a seamless framework from the global to the
coastal/littoral scale.
Slide 2
COSS-TT Schmitz (2005) Satellite SST 7-day composite 3/1998
Satellite SST 3-day composite 3/2001 From global to coastal: Loop
Current/Florida Current System Loop Current Florida Current Air-sea
interactions: heat reservoir for hurricanes
Slide 3
Walker et al., 2011 Schiller and Kourafalou (2014) Observing
and predicting coastal to offshore interactions
Slide 4
COSS-TT Strategy topic 1: Scientific support of the development
of Coastal Ocean Forecasting Systems (COFS) and applications Main
goal: Support research and development to advance coastal ocean
forecasting, through the integration of observations and models in
coastal areas and in synergy with larger scale observatories and
modeling systems.
Slide 5
(1) Monitoring of physical, and biogeochemical parameters in
coastal regions Multi-platform and interdisciplinary Coastal Ocean
Observing Systems Coastal/regional array design : OSE/OSSEs,
Representer Matrix Spectrum Monitoring needs in a variety of scales
(mesoscale / sub-mesoscale; short to long term) Fine scale
modeling: seamless transition from large to shelf to coastal and
estuarine scale Integration of coastal observing systems and fine
scale models Advances in policy: establishment of regional
observing systems Advances in instruments and methods (CA, SWOT)
Kourafalou et al. (2014a,b)
Slide 6
Toward the development of rigorous Ocean OSSEs Rigorous ocean
OSSE methodology: Nature run (NR) model: no DA - should reproduce
ocean climatology and the structure of ocean features with
pre-specified accuracy Forecast model (FM): with DA - run at lower
resolution (and different model) than the NR; errors between the FM
and NR should have similar magnitudes and properties as the
expected errors between the best available models and the true
ocean Synthetic observations sampled from the NR: Observed /
expected coverage, resolution, accuracy Realistic errors must be
added Rigorous OSSE system validation: Compare test OSSE with
similar OSE If results significantly different, calibration may be
necessary Ocean OSSEs are at an early stage (compared to
atmospheric OSSEs and ocean OSEs) and have suffered limitations
that may lead to biased results: Sometimes rely on the reference
simulation only (no DA - just sub-sampling reference simulation)
When DA, use of the same ocean model for reference (nature) run and
assimilated simulation (North Sea example)- (identical twin
issue)
Slide 7
Coastal domain: experiments in the North Sea (use DA, but same
model with NR) Rapid dynamics, wind-driven (coastal waves) test of
altimetry constellation and wide-swath altimetry (future
instrument) Instantaneous SSH ensemble variance (cm 2 ) before
analysis, after analysis based on classical altimeter data, and
after analysis based on wide-swath altimeter data. Wide-swath
altimetry increases the spatial extension of error reduction This
leads to improve the temporal control of errors Jason-1Wide- swath
altimeter Error reduction by Jason-1: 29.74% Error reduction by
wide-swath alt.: 52.57% Time evolution of the ensemble SSH variance
(cm 2 ), in a single location (north domain area), with impact of
the observing systems. Adapted from Mourre et al. (2006). Early
Ocean OSSE example Adapted from Mourre et al. (2006 )
Slide 8
Nature Run: presence of warm-core anticyclone (E) north of
Corsica during the 8-day simulation (not in Control Run) Impact of
3 sea gliders on the upper layer Temp.: unaware or coordinated
Unaware glider fleet: only 1 glider samples the eddy; eddy poorly
constrained Coordinated glider fleet: all 3 directed toward the
eddy; eddy well constrained Need for high resolution in situ
measurements for constraining oceanic mesoscale features Glider
OSSEs in the Ligurian Sea (adaptive sampling, four 2-day cycles)
ROMS (1.8 km resolution) EnKF with slightly different ROMS
configuration for FM & NR (fraternal twin experiment) EnKF: use
ensemble mean for: -Control Run (non-assimilated) -Estimated State
(assimilated) Oke et al. (2014) Mourre and Alvarez (2014 )
Slide 9
New development: Rigorous ocean OSSE prototype Development at:
Joint NOAA/Univ. Miami Ocean Modeling and OSSE Center (OMOC)
Co-Directors: G. Halliwell (NOAA/AOML) and V. Kourafalou
(UMiami/RSMAS) Advisory Board : R. Atlas and Gustavo Goni
(NOAA/AOML); Peter Ortner (UM/RSMAS/CIMAS) Key goals: Conduct
research to improve the quality of ocean analyses and forecasts
Perform observing system evaluation and design studies using OSEs
and OSSE The novel aspect of this work is: development of a
rigorously evaluated (and calibrated if necessary) ocean OSSE
system that will enable unbiased estimates of ocean observing
system impact. This system has succesfully applied for the first
time techniques that have long been in use for atmospheric OSSEs
but that had not been previously completely implemented for the
ocean.
Slide 10
Ocean OSSE System / OMOC prototype Incorporates all design
criteria and rigorous validation methods developed for atmospheric
OSSE systems Main goal: relocatable system
(regional/basin-wide/global; under the strategy of seamless ocean
obsrerving systems and forecasting) System initially validated in
the Gulf of Mexico / regional & coastal Initial application
evaluate airborne ocean profiling strategies Now expanded in North
Atlantic / basin-wide coupled modeling Demonstrate a relocatable
ocean OSSE system in an Atlantic Ocean domain for a broad range of
oceanographic problems Initial emphasis on observing system design
studies for improving coupled hurricane forecasts
Slide 11
OSSE System Gulf of Mexico Configuration COMPONENTS: Nature Run
(NR) free running HYbrid Coordinate Ocean Model (HYCOM) run at high
resolution (3-4 km) 2004-2010 unconstrained simulation of the Gulf
of Mexico Forecast Model (FM) with DA (fraternal twin system) The
FM is also HYCOM, but configured substantially differently from the
NR Different vertical coordinate type Different vertical mixing
scheme Different horizontal mixing and viscosity coefficients Run
at one-half the horizontal resolution Assimilates synthetic
observations simulated from the NR Synthetic Observation Simulation
Toolbox Realistic errors added on synthetic observations Halliwell
et al., 2014a,b Perform data denial experiments with observations
extrcacted from the NR uncertainties in actual observations vs FM
(instrument noise, representation of different scales in
FM/obs.)
Slide 12
OMOC GoM OSSE example: evaluate the impact of DwH observations
(temporal/spatial sampling design of NOAA air-borne WP-3D) Typical
sampling pattern (0.5 0 ) of the WP-3D hurricane research aircraft
during the DWH oil spill (dropping AXBTs, AXCTDs AXCPs)
(Observations collected by N. Shay and colleagues at UM/RSMAS)
Sampling pattern for the OSSE: synthetic AXCTDs sampling T and S
profiles (0-1000 m) Main Question: How can we optimize the positive
impact of future rapid-response airborne surveys? OSSEs are
performed to address this question
Slide 13
Impact Assessments Relevant to particular application:
hurricane forecasting Tropical Cyclone Heat Potential (TCHP)
relative to 26C isotherm (the thermal energy required to heat all
near-surface water above 26C from 26C to the observed temperature)
- Also known as Ocean Heat Content (OHC) Sea surface height (SSH)
Important to reduce errors in the structure of energetic ocean
features (eddies and boundary currents) Background
quasi-geostrophic flow field associated with these features
distorts the SST cooling pattern forced by hurricanes Emphasize
error reduction in two hurricane-related metrics:
Slide 14
Rigorous Evaluation of the OMOC OSSE System 1.The NR model must
reproduce both the climatology and variability of ocean phenomena
of interest within pre-specified error limits 2.Errors between the
DA and NR models must have similar magnitude and properties as
errors between the best available ocean models and the true ocean
(models must be different) 3.OSSE system errors and biases must be
evaluated by comparing OSSEs to reference OSEs Comparison of
OSE/OSSE pairs that are identical (except that one assimilates real
obs. and the other assimilates synthetic obs.) demonstrates that
OSSEs produced by the OMOC Gulf of Mexico OSSE system will produce
unbiased impact assessments use various altimetry and in situ
observation scenarios validate along temperature profiles No DA
Deny P3, 2(3) alt Deny P-3 All obs. Evaluate sensitivity: daily
time series of RMS error with respect to the NR calculated over the
synthetic obs. box
Slide 15
OSSE system Application in the GoM Impact assessments focus on
four questions: Q1 - Overall impact of assimilating airborne
profiles Q2 - Impact of horizontal profile resolution Q3 - Impact
of probe type Q4 - Impact of time interval between airborne surveys
Control experiment assimilates 0.5 profiles of temperature and
salinity from synthetic AXCTDs to 1000 m over two days prior to
each analysis time Analyses run every 7 days May October 2010
Idealized airborne survey patterns on the 0.5 grid (all points) and
the 1.0 grid (large points only) Large area chosen to obtain robust
statistics.
Slide 16
Q2: Impact of Horizontal Profile Resolution Decreasing
horizontal resolution has a large impact on RMS errors of both SSH
and TCHP Higher-resolution profiling corrects variability with
smaller horizontal scales that are not well constrained by
satellite altimetry. Deny synthetic profiles 1.0 resolution 0.5
resolution (control) Deny synthetic profiles 1.0 resolution 0.5
resolution (control)
Slide 17
Q3: Impact of Probe Type Assimilating 400 m AXBTs instead of
1000 m AXCTDs results in a modest increase in SSH RMS errors but no
increase in TCHP errors Deny synthetic profiles 400 m AXBT Deny
synthetic profiles 1000 m AXCTD (control) 400 m AXBT 1000 m AXCTD
(control)
Slide 18
New Atlantic Ocean OSSE Domain Contains North Atlantic
hurricane domain (far away from boundaries) Initial application is
observing system evaluation for hurricane applications
Slide 19
Evaluation Criteria for the NR and FM Ocean Models Realism
criteria for NR model Simulate realistic ocean climatology and
variability Realism criteria for FM model Must be a less-capable
model, but cannot be too unrealistic Comparative performance
criteria Error growth rates between NR and FM unconstrained runs
due to physics and truncation errors must be similar to error
growth rates between the best ocean models and the true ocean This
criterion cannot be rigorously evaluated Evaluation, therefore,
based on three less-stringent criteria: (1) Initial RMS error
growth between the two models reaches saturation levels within a
realistic time interval (2) RMS error saturation levels between the
two models are similar to error levels between the NR and
observations (3) Mean biases in the FM with respect to the NR have
similar magnitudes to mean biases in the NR with respect to
observations
Slide 20
Evaluation: Other Criteria NR and FM model mean climatologies
Model realism criterion Compare NR and FM mean fields to observed
climatologies Comparative performance criterion Are biases between
the NR and FM realistic? Compare bias fields NR mean minus observed
climatology FM mean minus observed climatology NR mean minus FM
mean NR and FM variability Model realism criterion Compare NR and
FM RMS amplitude fields to climatological RMS amplitudes
Comparative performance criterion compare RMS difference maps NR
vs. observations FM vs. observations NR vs. FM
Slide 21
Wide range of space and time scales and processes to observe.
Need to develop and evaluate both global and regional nature runs
NR availability (community shared NR) Computationally expensive, as
models advance in higher resolutions (esp. if advanced DA schemes,
2 models to evaluate, ensemble methods) Observational limitations:
o data availability to evaluate NR (different challenges for large
scale/coastal data sets) o altimetry observations and Argo floats
are the main operational ocean data Forcing limitations: Data
assimilation algorithms (coastal DA is an active research topic)
Ocean OSSEs: challenges going forward o often not clear, as most
recent/presumably updated forcing sets might not have undergone
evaluation o different resolution/frequency needs in large vs.
coastal scale)
Slide 22
International collaboration is very advantageous for the
development of ocean OSSEs; OSEval-TT under GODAE/OceanView should
play a key role, in collaboration with COSS-TT global and regional
modeling and observing systems atmospheric OSSEs and other
international initiatives Good strategy:well-defined problem to
address (broad range of specific cases possible). Ocean OSSEs:
activities going forward OSSE session at 2015 American
Meteorological Society (Phoenix, Arizona) 19th Conference on
Integrated Observing and Assimilation Systems for Atmosphere,
Oceans, and Land Surface (IOAS-AOLS)
Slide 23
Additional slides
Slide 24
Observation TypeObserving System Random Instrument Error
Representation Error Other Errors Satellite SSH Jason-1
altimeter0.03mrandom 0.08m length scale 100km Internal tides random
0.03m length scale 5 km Jason-2 altimeter0.03mrandom 0.08m length
scale 100km Internal tides random 0.03m length scale 5km Envisat
altimeter0.03mrandom 0.08m length scale 100km Internal tides random
0.03m length scale 5km Satellite SST MCSST 0.12Crandom 0.05 C
In-situ SST Surface Buoy 0.05 Crandom 0.05 C Surface Drifter 0.05
Crandom 0.05 C Ship intake 0.12 Crandom 0.05 C XBT T profiles
Ship0.02 CRandom 0.02CDepth error RMS ampl. 2m length scale is full
profile depth Airborne (WP-3D)0.02 C Random 0.02 CDepth error RMS
ampl. 2m length scale is full profile depth XCTD T,S profiles
Airborne (WP-3D)T 0.02 C S 0.05PSU Random T 0.02 C S 0.05PSU Depth
error RMS ampl. 2m length scale is full profile depth XCP T, u, and
v profiles Airborne (WP-3D)T 0.02 C u 0.03 m s -1 v 0.03 m s -1
Random T 0.02 C u 0.03 m s -1 v 0.03 m s -1 Depth error RMS ampl.
2m length scale is full profile depth Summary of the errors added
to the synthetic observation types and observing systems sampled
from the NR. Although velocity profiles were not assimilated,
errors were added to AXCP velocity profiles since they were used
for evaluation.