Development of an “end-to-end” altimeter mission simulator

1. Development of an “end-to-end” altimeter mission simulator Alix Lombard - Juliette Lambin (CNES)Laurent Roblou – Julien Lamouroux (NOVELTIS)

2. Context • Debates on future altimetry constellation design • need for continuity and complementarity between missions • variety of applications (climate, meso-scale, operational,…) but all need multi-mission • orbit : sun-synchronous or not, cycle/repetitivity, existing tracks or not, … • payload : bi-frequency or not, radiometer or not, platform stability (roll for wide-swath altimeter), … • data : sampling, latency/availability, … • Need for a decision-making tool :”End-to-end” mission simulator (R&D CNES funding) • objective : examine the merits of various observing configurations / discriminate among them • need for a simple, flexible, evolutive tool

3. Multi-missions obs. systems Altimetry configuration performances Sampler (pseudo- observations) Analyzer (assimilation) Storm surges (model) Status : end-to-end altimeter mission simulator for storm surge observations • Possibility of studying multi-missions altimetry configurations, easy tuning of orbit configurations parameters • Framework ofObserving-Systems Simulation Experiments(OSSEs, Arnold and Dey, 1986) :designed to evaluate the impact of observing system data in numerical analysis. • “Ensemble Twin Experiments” method (Mourre et al., 2004) : • pseudo-observations generated from a “control” simulation (oceanic model) • then assimilated in a “free” simulation The performance of the system is estimated in terms of model error (=ensemble variance) reduction performed via a data assimilation system.

4. Methodology • Model configuration : MOG-2D / T-UGO 2D (F. Lyard) • barotropic, non linear, finite element • zone : well known / studied and representative / varied (open ocean, shelf and coastal seas) • time period : 15 days, typical / varied winter storm surges conditions (16/11 to 01/12/1999) • atmospheric forcing: surf. pressure / 10m-wind (ARPEGE) • tidal forcing • Generation of pseudo-observations • Altimetry configuration set up by user (specify orbit parameters) • pseudo-obs. (Sea Level Anomaly) extracted from the model reference simulation (non-perturbed run), at the space-time altimetry positions • then noise-added (gaussian noise of 0-mean and standard deviation specified by instrument noise level) Nadir Wide swath • Model errors computation (prior requirement for data assimilation) • estimated from a 100 Ensemble simulations of the model in response to atmospheric forcing errors (surf. pressure and 10m-wind perturbed) [Lamouroux, 2006] • error statistics thus estimated by the ensemble variance of the model at each analysis time step (daily) – errors variable in time and space 11 cm² 20/11 0 cm²

5. Methodology • Data assimilation / Performance analysis method • s-EnROOI (simplified Ensemble Reduced Order Optimal Interpolation) configuration • “simplified” : no sequential control of the model (ensemble error reduction only estimated at analysis time, not propagated in time via the model) → quick execution / results obtained • Possibility to implement EnROOI, ROEnKF, EnKF (higher performance but longer computational time)… but idea to keep a simple / quick decision-making tool to discriminate between various observing scenario • SEQUOIA + MANTA codes used (De Mey, 2005) Perturbed simulations Model reference simulation Ensemble variance reduction estimation at each analysis time step

6. 100 % 11 cm² 11 cm² 80 50 0 cm² 0 cm² Validation • “Ideal” observing system • regularly spaced grid • pseudo-obs / analysis daily • Results for Ta = 20/11/1999 (analysis time representative of model errors over the whole period) • Time-averaged result Ensemble variance of the model (before correction) Ensemble variance after pseudo-obs. assimilation % of ensemble variance reduction over the period  Over the whole period (synthetic gain ~ 78%), methodology validated  Strong anduniform reduction of variance, especially in the English Channel (gain Ta ~ 94%)

7. SWOT on a JASON orbit JASON-1 100 70 40 Performance of various altimetry configuration • Various performance diagnostics • at each analysis time step, mapped • synthetic over the period, space averaged …  Efficient tool to estimate the performances of various altimetry configuration and to discriminate among them.  Allow to design orbit and assess performances of multi-satellite altimetry systems Reduction of ensemble variance time-averaged over the period NB: the higher the percentage of variance reduction, the more the altimeter mission will provide helpful information to storm surges models Lamouroux et al, OSTST meeting, Hobart, 2007

8. Multi-missions obs. systems Tides aliasing diagnostics Tides Altimetry configuration performances Sampler Analyzer Storm surges Oceanic circulation Evolution : end-to-end altimeter mission simulator for the study of tide aliasing question • Context of possible sun-synchronous orbit configurations (SWOT, Jason-3, Sentinel-3, …) → tide aliasing problem Existing module • Extension work in progress

9. Evolution : end-to-end altimeter mission simulator for the study of tide aliasing question • Same methodology but some evolutions needed → some work done, some in progress • Ocean tide model configuration : T-UGO 2D • 28 ocean tide components, model validated through comparisons with FES2004 / GOT00b • larger zone (long wave dynamics of ocean tides) • 1-year simulation • model dissipation parameters : topography, bottom friction coefficient, transfer coefficient towards barocline modes • Generation of pseudo-observations : • ocean tidemodel reference simulation (non-perturbed run) → high frequency (HF) • lower frequency (LF) ocean circulation simulation (daily reanalysis from PSY2V2 global ocean model computed by MERCATOR-Ocean) • pseudo-obs. extracted from the sum of both simulations (ocean tide HF + ocean circulation LF), at the space-time altimetry positions → take into account the coupling HF aliased by altimetry sampling at LF / LF circulation • Model errors computation : • estimated from ensemble simulations of the model in response to perturbed model dissipation parameters • work in progress

10. Conclusions and perspectives • Work in progress for tidal analysis (end of R&D funding + SWOT PASO study) • ensemble model error statistics (ensemble variance) computation • implementation of specific tide aliasing diagnostics • more realistic observation errors to be defined (especially for wide-swath altimeter) • case studies (inferred from PASO SWOT instrument study) • First prototype of the simulator (storm-surge model) • efficient tool to estimate the performances of various altimetry configuration and discriminate among them • simple, highly flexible and evolutive, first version of a powerful tool for designing orbit for multi-satellite altimetry systems (Jason-3, SWOT, Sentinel-3 …) • Work plan / Perspectives • further tests of altimetry configurations : case studies, ≠ realistic mission scenario tests • implement other oceanic processes : ocean circulation, waves, … • implement more complex data assimilation scheme : e.g. for refined studies