Real-Time ROMS Ensembles and adaptive sampling guidance

during ASAP

Sharanya J. Majumdar

RSMAS/University of MiamiCollaborators: Y. Chao, Z. Li, J. Farrara,

P. Li, P. Lermusiaux, C. Bishop

ASAP Hot Wash, 11/1/06-11/3/06

Why Use Ensembles?

• To quantify uncertainty in flow– Degree of confidence in prediction– Probabilistic forecast

• Adaptive Sampling– Use ensemble-based error statistics to predict

locations in which extra sampling is required

• Data Assimilation– Flow-dependent error covariance matrix

• Synoptic and Sensitivity Analysis

Progress Prior to ASAP

• Software developed at JPL to produce ensembles of 3-nested ROMS– Atmospheric wind stress perturbations

– Oceanic initial condition perturbations (breeding)

• What we learned (2003-5)– 3-nested ROMS cumbersome (7 forecasts per day)

– Realistic atmospheric perturbations produce minimal change in 48-hour ROMS forecast

– Higher sensitivity to initial ocean conditions

ROMS ensembles in ASAP

• Goal: to provide automated real-time daily ensembles and adaptive sampling guidance

• Single-domain ROMS– 1.67km resolution– Lateral boundary conditions provided by

average of operational 3-nested ROMS forecast– No atmospheric wind stress perturbations– Initial condition perturbations produced by

‘breeding’ technique

• 32-member ensemble

ROMS Analysis and Forecast

Forecast perturbations1-day ROMS ensemble forecast from previous day

Rescale to yield Analysis Perturbations

Variance and ETKF data files and graphics uploaded to OurOcean

ETKF adaptive sampling

Post-process ensemble

New initial ensemble

2-day ROMS ensemble forecast2-day COAMPS wind forecast

Timeline

• 29 July-10 Aug: added new ensemble members.

• 13 Aug-13 Sep: Daily 32-member ROMS ensembles available by 9am PDT on http://ourocean.jpl.nasa.gov/MB06/

• Fully automated.

Ensemble Variance

• Prediction of ‘uncertainty’ in a forecast.

• Next few slides show 48-h forecast mean and variance fields for ensembles initialized between 22-28 August 2006.

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Ensemble Transform Kalman Filter (ETKF) adaptive sampling

Q: In what location should we collect and assimilate extra observational data, in order to improve an X-hour forecast? (X=0,24)

ttii ttoottvv

EnsembleEnsemble

Initialization timeInitialization time

Adaptive Adaptive

Sampling timeSampling timeForecast timeForecast time

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Observe T and S on 23 Aug 2006

Observe T and S on 24 Aug 2006

Observe T and S on 25 Aug 2006

Observe T and S on 26 Aug 2006

Observe T and S on 27 Aug 2006

Observe T and S on 28 Aug 2006

Observe T and S on 29 Aug 2006

Review of Performance

• Automation and timely delivery worked well.

• Variance and adaptive sampling guidance seemed qualitatively reasonable.

• Cut corners: no perturbations in lateral boundary conditions, wind stress, heat flux etc.

The Future: Short Term

• Re-run ROMS ensemble for 2003 and 2006– Using new ROMS reanalysis– Stable analysis error variance?– Is ensemble variance a good predictor of

forecast error?

• Evaluate ETKF adaptive sampling– Qualitative evaluation of sensitive areas– Quantitative evaluation of whether ETKF can

predict reduction in forecast error variance (using ROMS data denial)

Papers to be completed

1. ROMS ensembles: AOSN-II and ASAP

2. ETKF adaptive sampling, interpretation and evaluation of guidance

3. Adaptive sampling review, comparison of ESSE and ETKF

4. Response of ocean model to changes in atmospheric forcing

The Future: Long Term

• Observing System Simulation Experiments– Couple adaptive sampling guidance to AUV

survey error metrics (Zhang/Bellingham)– Test hypothetical configurations of glider

arrays (Leonard, Lermusiaux)– Work with REMUS AUV (Moline)