GOVST-V Capital Hotel, Beijing, China, Oct 2014 Lecce, Italy, Feb 4-7, 2012 Links between OSEval and COSS Villy Kourafalou Univ. of Miami/RSMAS OSEval-TT Oct 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.

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

Walker et al., 2011 Schiller and Kourafalou (2014) Observing and predicting coastal to offshore interactions

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.

(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)

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)

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 )

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 )

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.

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

OSSE System Gulf of Mexico Configuration  COMPONENTS: Nature Run (NR) – free running HYbrid Coordinate Ocean Model (HYCOM) run at high resolution (3-4 km) 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.)

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 ( m) Main Question: How can we optimize the positive impact of future rapid-response airborne surveys?  OSSEs are performed to address this question

Impact Assessments Relevant to particular application: hurricane forecasting Tropical Cyclone Heat Potential (TCHP) relative to 26ºC isotherm (the thermal energy required to heat all near-surface water above 26°C from 26°C 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:

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

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 0.5º 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 0.5º grid (all points) and the 1.0º grid (large points only) Large area chosen to obtain robust statistics.

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)

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)

New Atlantic Ocean OSSE Domain Contains North Atlantic hurricane domain (far away from boundaries) Initial application is observing system evaluation for hurricane applications

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

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

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)

 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)

Additional slides

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 0.12ºCrandom 0.05 ºC In-situ SST Surface Buoy 0.05 ºCrandom 0.05 ºC Surface Drifter 0.05 ºCrandom 0.05 ºC Ship intake 0.12 ºCrandom 0.05 ºC XBT T profiles Ship0.02 ºCRandom 0.02ºCDepth error RMS ampl. 2m length scale is full profile depth Airborne (WP-3D)0.02 ºC Random 0.02 ºCDepth error RMS ampl. 2m length scale is full profile depth XCTD T,S profiles Airborne (WP-3D)T 0.02 ºC S 0.05PSU Random T 0.02 ºC 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 ºC u 0.03 m s -1 v 0.03 m s -1 Random T 0.02 ºC 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.