Variational data assimilation of open sea and coastal chlorophyll in the Mediterranean Sea operational forecast system

Transcription

1 Variational data assimilation of open sea and coastal chlorophyll in the Mediterranean Sea operational forecast system Anna Teruzzi*, Gianpiero Cossarini, and Cosimo Solidoro 1st GODAE OceanView DA-TT Workshop Exeter, May 2015

2 Coastal assimilation in biogeochemical model Motivation for coastal assimilation? Better description of biogeochemical processes for the definition of the environmental status (European MSFD) Which elements are required? Specific developments of assimilation Satellite estimation of coastal chlorophyll (Copernicus) Increased model resolution These elements are today available

3 Outline Current forecasting system of the Mediterranean Sea biogeochemistry 3DVAR coastal data assimilation Results and comparison of tests with different configuration Conclusion and further developments

4 Mediterranean biogeochemical forecast system Part of Copernicus European Programme

5 Mediterranean BGC forecast system 3DVAR assimilation (Teruzzi et al. 2014) BGC model BFM model for low trophic level Multi-nutrient, multi-plankton Offline coupled with OGCM (INGV, Bologna) Observations Surface chlorophyll concentration MODIS ocean colour Mediterranean Sea algorithm (GOS ISAC CNR, Rome) Corrections on phytoplankton state variables Weekly assimilation Operational applications and reanalysis (OPEC, MyOcean, Copernicus) Teruzzi et al. shows improvement of forecast performances at different spatial and temporal scales

6 3DVAR assimilation Minimization of the cost function J J 1 T 1 1 xk dxk Bk dxk dk H 2 2 In order to obtain correction dx T 1 d dx R d Hdx B and R are the background and observations error covariance Background error covariance matrix preconditioning and decomposition (Dobricic and Pinardi, 2008) B = V V T with V = Vv Vh Vb k k k k Teruzzi et al. 2014

7 Error covariance decomposition B=Vv Vh Vb Vertical operator Vv Covariance along the vertical described by means of chlorophyll profiles (EOF based on model run) Horizontal operator Vh Covariance in the horizontal direction consist of a Gaussian smoother (15km) Biogeochemical operator Vb Covariance among the phytoplankton variables with preservation of the physiological status chl P N C Teruzzi et al P1 P2 P3 P4

8 Error covariance decomposition B=Vv Vh Vb Vertical operator Vv Covariance along the vertical described by means of chlorophyll profiles (EOF based on model run) B = V V T with V = Vv Vh Vb Teruzzi et al Horizontal operator Vh Covariance in the horizontal direction described by a Gaussian smoother Biogeochemical operator Vb Covariance among the phytoplankton variables with preservation of the physiological status Current DA scheme works for operational and reanalyses applications in areas with sea depth greater than 200 m

9 Error covariance decomposition B=Vv Vh Vb Vertical operator Vv Covariance along the vertical described by means of chlorophyll profiles (EOF based on model run) B = V V T with V = Vv Vh Vb Teruzzi et al Horizontal operator Vh Covariance in the horizontal direction described by a Gaussian smoother Biogeochemical operator Vb Covariance among the phytoplankton variables with preservation of the physiological status Assimilation of open sea and coastal chlorophyll concentration

10 Vv for coastal assimilation K-means on surface chlorophyll to identify coastal regions with different dynamics February June In June regions definition depends on local effects (Po river in the Adriatic Sea)

11 Vv for coastal assimilation EOF profiles on k-means regions calculated for different sub-regions Uniform profiles in winter February June In summer 1 or 2 maxima Different depth in the k-means classes

12 Vh for coastal assimilation Lx Normalized with the L open sea

13 Coastal and open sea assimilation runs Open sea assimilation based on Teruzzi et al Costal assimilation with modified Vv and Vh CR DA01 DA02 Open sea assimilation - Yes Yes Coastal assimilation - - Yes Experiment for year 2011 with spatial resolution 1/16

14 Comparison with satellite chlorophyll Map of surface chlorophyll (mg chl/m 3 ) 22 March SAT DA01 Relevant effects in the Adriatic Sea and in the Gulf of Gabez DA02

15 Comparison with satellite chlorophyll Map of surface chlorophyll (mg chl/m 3 ) 7 June SAT DA01 Relevant effects in the Adriatic Sea and in the Gulf of Gabez DA02

16 Comparison with satellite chlorophyll Taylor diagrams NWM coastal area in Spring Most of the improvement introduced by DA01 CR DA01 DA02

17 Comparison with satellite chlorophyll CR Taylor diagrams Northern Adriatic in Spring Improvement introduced by DA02 DA01 DA02

18 Comparison with in situ data Annual mean of chlorophyll in Adriatic Sea CR DA02

19 Comparison with in situ data Annual mean of chlorophyll in Adriatic Sea CR DA01 DA02 SAT X In situ DA results consistent with in situ observations Southern Adriatic Northern Adriatic

20 Conclusion Use of 3DVAR for assimilation of coastal products Improved results for chlorophyll also in comparison with in situ data Future steps Comparison with in situ nutrients Further improvement of Vh (anisotropy) Observation errors definition

21 Thank you for your attention 1st GODAE OceanView DA-TT Workshop Exeter, May 2015

22 Comparison with satellite chlorophyll Time series of mean chlorophyll on coastal sub-region (mg chl/m 3 ) Coast SAT CR DA01 DA02 Coastal assimilation introduces significant corrections

23 Mediterranean Sea Enclosed sea Coastline urbanization Percentage of areas with depth < 200m North Adriatic Sea Knowledge of the coastal phenomena Biogeochemical dynamics