Real-Time ROMS Ensembles and adaptive sampling guidance during ASAP Sharanya J. Majumdar RSMAS/University of Miami Collaborators: 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 perturbations 1-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

Initial perturbation method We have a 24-h ROMS forecast ensemble. Compute perturbations about ensemble mean. Re-scale these perturbations by a scaling factor consistent with analysis error variance (~0.8) Add scaled perturbations to ROMS analysis to yield initial ensemble Integrate this ensemble forward 2 days.

Timeline 29 July-10 Aug: added new ensemble members. 13 Aug-13 Sep: Daily 32-member ROMS ensembles available by 9am PDT on 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 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) titititi totototo tvtvtvtv Ensemble Initialization time Adaptive Sampling time Forecast time t 24 hours

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)