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

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

Analyzer (assimilation) Sampler (pseudo- observations) Multi-missions obs. systems Storm surges (model) Altimetry configuration performances 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 of Observing-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.

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 NadirWide swath  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)  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 0 cm² 20/11

 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) Methodology Model reference simulation Ensemble variance reduction estimation at each analysis time step Perturbed simulations

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

Performance of various altimetry configuration SWOT on a JASON orbit JASON-1  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 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  Various performance diagnostics  at each analysis time step, mapped  synthetic over the period, space averaged … Reduction of ensemble variance time-averaged over the period

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

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  Generation of pseudo-observations :  ocean tide model 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  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  Model errors computation :  estimated from ensemble simulations of the model in response to perturbed model dissipation parameters  work in progress

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