The PML data assimilation system For the North East Atlantic and ocean colour. Stefano Ciavatta, Ricardo Torres, Stephane Saux-Picart, Icarus Allen
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1 The PML data assimilation system For the North East Atlantic and ocean colour Stefano Ciavatta, Ricardo Torres, Stephane Saux-Picart, Icarus Allen OPEC, Dartington, December 2012
2 Outline Main features of the assimilation system Preliminary results: twin experiment Computational requirements What s next
3 Main features of the DA system L4 biogeoch. Data MODIS chlorophyll Model ERSEM-POLCOMS DA WCO NEODAAS-RSG RSG-NEODAAS Lewis and Allen, 2007 Localized Ensemble Kalman Filter (log-transformation, 100 members) MODIS 5-day composites of chlorophyll Weekly assimilation (year 2006)
4 Main features of the DA system Root mean square error vs satellite chlorophyll Model Assimilation RMSE (µg/l) Total particulate carbon at L4 Improved yearly and seasonal simulation of carbon-cycle variables RMSE=144 mgc/m -3 RMSE=133 mgcm -3 RMSE = -7.7% Model Assimilation Time L4 data Other 11 time series, Other 3 skill metrics ok, but not short term (ME) Ciavatta et al., J. Geophys. Res., 2011
5 Main features of the DA system Model: POLCOMS-ERSEM 49 state variables (including carbonate system) + 1 passive tracer Benthic module: on Satellite data: GlobColour chlorophyll from 1997 to Data errors are available pixel x pixel as percentage standard deviation Resolution: 4 km, reprocessed to 9 km grid Daily product are used to compute 5 days composites centred on the assimilation date Assimilation frequency: monthly
6 Main features of the DA system Assimilation: Ensemble Kalman filter [Evensen, 2003] 100 members Log-transformation of states and observations Local analysis. Radius variable in space as a function of the bathimetry: - depth < 50 m : radius = m (14%) - 50 m < depth < 2000 m: radius = m (51%) - depth > 2000m : radius = m (35%) Depth m
7 Analysed 39 Main out features of 49 variables of the (max DA system is 41): (2/3) Nutrients: N1p, N3n, N4n, N5s Phytoplankton types: Chl1, Chl2, Chl3, Chl4, P1c, P1n, P1p, P1s, P2c, P2n, P2p, P3c, P3n, P3p, P4c, P4n, P4p Zooplankton types (only C): Z4c, Z5c, Z6c, Bacteria (only C): B1c, Detritus: R1c, R1n, R1p, R2c, R4c, R4n, R4p, R6c, R6n, R6p, R6s, R8c Carbonate sys: O3c, bioalk Ciavatta et al., JGR, 2011
8 Main features of the DA system Hyper-parameters: Model error (forecast time): Gaussian pseudo-random perturbation of the input irradiance values (stand dev: 20% of the irradiance value) Model error (analysis time): pseudo-random Gaussian perturbations of the 3D fields [Evensen, 2003] of all the analysed variables (stand dev: 10% of the values of the variables) Observational error: pseudo-random Gaussian perturbations of the 2D fields [Evensen, 2003] of the total chlorophyll data (stand dev: XX% of the chl values)
9 Twin experiment ( year 2000 ) Spatial distributions (April) Forecast Reference Analysis Tot. chlorophyll (assimilated) Nitrate mg m -3 mmol m -3
10 Twin experiment ( year 2000 ) Forecast Reference Analysis Year evolutions OC L4 NS Ocean (OC) North Sea (NS) L4 Chlorophyll [mg m -3 ] Note: different scales Nitrate [mmol m -3 ] Months
11 Twin experiment ( year 2000 ) Forecast Reference Analysis Vertical profiles OC L4 NS Ocean (OC) North Sea (NS) L4 Depth (level) Depth (level) Chlorophyll [mg m -3 ] Nitrate [mmol m -3 ] Note: different scales Concentration
12 Technical notes computational requirements Forecast step: - 1 member x 1 node = 32 cpus members x 100 nodes: 3200 cpus - Walltime x 1 month (100 members in parallel) = 02:10 hours - Estimated time x 1 year = about 26 hours (without queuing) Analysis step: members, 39 variables : requires about 30 GB RAM - uses 5 nodes (mppwidth=160 cpus): - Walltime x 1 analysis = 01:20 hours - Estimated time x 1 year = about 16 hours (without queuing) 1 year 42 hours
13 What s next Investigate further the system features (twin exp) Set-up outputs for skill assessment Assimilation of real GlobColor data!
14 Thank you
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