Application of an Ensemble Optimal Interpolation in a North/Baltic Sea model: Assimilating temperature and salinity profiles

2011 ◽  
Vol 40 (3-4) ◽  
pp. 227-245 ◽  
Author(s):  
Weiwei Fu ◽  
Jun She ◽  
Shiyu Zhuang
Keyword(s):  
Baltic Sea ◽  
Optimal Interpolation ◽  
Ensemble Optimal Interpolation

Sea level variations in the coastal region from altimetry – Using optimal interpolation in the Baltic Sea for 3-day mean sea level from altimetry, tide gauge and model data

2021 ◽  
Author(s):  
Ida Margrethe Ringgaard ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Adili Abulaitijiang ◽  
Ole B. Andersen
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Temporal Resolution ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Coastal Region ◽  
Optimal Interpolation ◽  
The Baltic Sea ◽  
Spatial Coverage ◽  
The Baltic

<p>The rise and fall of the sea surface in the coastal region is observed closely by two different sources: tide gauges measure the relative sea level anomaly at the coast at high temporal resolution (minutes or hours) and satellite altimeters measure the absolute sea surface height of the open ocean along tracks multiple times a day. However, these daily tracks are scattered across the Baltic Sea with each track being repeated at a lower temporal resolution (days). Due to the inverse relationship between spatial and temporal coverage of the satellite altimetry data, gridded satellite altimetry products often prioritize spatial coverage over temporal resolution, thus filtering out the high sea level variability. In other words, the satellite data, and especially averaged products, often miss the daily sea level variability, such as storm surges, which is most important for all societies in the coastal region. To compensate for the sparse spatial coverage from satellite altimetry, we here present an experimental product developed as part of the ESA project Baltic+SEAL:  on a 3-day scale, the DMI Optimal Interpolation (DMI-OI) method is combined with error statistics from a storm surge model as well as 3-day averages from both tide gauge observations and satellite altimetry tracks to generate a gridded sea level anomaly product for the Baltic Sea for year 2017. The product captures the overall temporal evolution of the sea level changes well for most areas with an average RMSE wrt. tide gauge observations of 17.2 cm and a maximum of 34.2 cm. Thus, the 3-day mean gridded product shows potential as an alternative to monthly altimetry products, although further work is needed.</p>

scholarly journals Nutrient transports in the Baltic Sea – results from a 30-year physical–biogeochemical reanalysis

2017 ◽  
Vol 14 (8) ◽  
pp. 2113-2131 ◽  
Author(s):  
Ye Liu ◽  
H. E. Markus Meier ◽  
Kari Eilola
Keyword(s):  
Baltic Sea ◽  
Cross Sections ◽  
Ocean Model ◽  
Optimal Interpolation ◽  
Nutrient Concentrations ◽  
Biogeochemical Model ◽  
The Baltic Sea ◽  
Gulf Of Riga ◽  
Baltic Proper ◽  
The Baltic

Abstract. Long-term oxygen and nutrient transports in the Baltic Sea are reconstructed using the Swedish Coastal and Ocean Biogeochemical model (SCOBI) coupled to the Rossby Centre Ocean model (RCO). Two simulations with and without data assimilation covering the period 1970–1999 are carried out. Here, the weakly coupled scheme with the Ensemble Optimal Interpolation (EnOI) method is adopted to assimilate observed profiles in the reanalysis system. The reanalysis shows considerable improvement in the simulation of both oxygen and nutrient concentrations relative to the free run. Further, the results suggest that the assimilation of biogeochemical observations has a significant effect on the simulation of the oxygen-dependent dynamics of biogeochemical cycles. From the reanalysis, nutrient transports between sub-basins, between the coastal zone and the open sea, and across latitudinal and longitudinal cross sections are calculated. Further, the spatial distributions of regions with nutrient import or export are examined. Our results emphasize the important role of the Baltic proper for the entire Baltic Sea, with large net transport (export minus import) of nutrients from the Baltic proper into the surrounding sub-basins (except the net phosphorus import from the Gulf of Riga and the net nitrogen import from the Gulf of Riga and Danish Straits). In agreement with previous studies, we found that the Bothnian Sea imports large amounts of phosphorus from the Baltic proper that are retained in this sub-basin. For the calculation of sub-basin budgets, the location of the lateral borders of the sub-basins is crucial, because net transports may change sign with the location of the border. Although the overall transport patterns resemble the results of previous studies, our calculated estimates differ in detail considerably.

Assimilating Altimetry Data into a HYCOM Model of the Pacific: Ensemble Optimal Interpolation versus Ensemble Kalman Filter

2010 ◽  
Vol 27 (4) ◽  
pp. 753-765 ◽  
Author(s):  
Liying Wan ◽  
Laurent Bertino ◽  
Jiang Zhu
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Computational Cost ◽  
Optimal Interpolation ◽  
Striking Difference ◽  
Primary Concern ◽  
Altimetry Data ◽  
Ensemble Optimal Interpolation ◽  
The Pacific

Abstract The ensemble Kalman filter (EnKF) has proven its efficiency in strongly nonlinear dynamical systems but is demanding in its computing power requirements, which are typically about the same as those of the four-dimensional variational data assimilation (4DVAR) systems presently used in several weather forecasting centers. A simplified version of EnKF, the so-called ensemble optimal interpolation (EnOI), requires only a small fraction of the computing cost of the EnKF, but makes the crude assumption of no dynamical evolution of the errors. How do both these two methods compare in realistic settings of a Pacific Ocean forecasting system where the computational cost is a primary concern? In this paper the two methods are used to assimilate real altimetry data via a Hybrid Coordinate Ocean Model of the Pacific. The results are validated against the independent Argo temperature and salinity profiles and show that the EnKF has the advantage in terms of both temperature and salinity and in all parts of the domain, although not with a very striking difference.

scholarly journals Nutrient cycling in the Baltic Sea – results from a 30-year physical-biogeochemical reanalysis

2016 ◽  
Author(s):  
Ye Liu ◽  
H. E. Markus Meier ◽  
Kari Eilola
Keyword(s):  
Baltic Sea ◽  
Ocean Model ◽  
Optimal Interpolation ◽  
Nutrient Concentrations ◽  
Biogeochemical Model ◽  
Phosphorus Transport ◽  
The Baltic Sea ◽  
Gulf Of Riga ◽  
Baltic Proper ◽  
The Baltic

Abstract. The long-term oxygen and nutrient cycles in the Baltic Sea are reconstructed using the Swedish Coastal and Ocean Biogeochemical model (SCOBI) coupled to the Rossby Centre Ocean model (RCO). Two simulations covering the period 1970–1999 are carried out with and without data assimilation, respectively. Here, the "weakly coupled" scheme with the Ensemble Optimal Interpolation (EnOI) method is adopted to assimilate the observed profiles in the reanalysis system. The simulation results show considerable improvements in both oxygen and nutrient concentrations in the reanalysis relative to the free run. Further, the results suggest that the assimilation of biogeochemical observations has a significant effect on the simulation of the oxygen dependent dynamics of biogeochemical cycles. From the reanalysis, nutrient transports between subbasins, between the coastal zone and the open sea, and across latitudinal and longitudinal cross sections, are calculated. Further, bottom areas of nutrient import or export are examined. Our results emphasize the important role of the Baltic proper for the entire Baltic Sea, with large net exports of nutrients into the surrounding subbasins (except the phosphorus transport into the Gulf of Riga and the nitrogen transports into the Gulf of Riga and Danish Straits). In agreement with previous studies, we found that the Bothnian Sea imports large amounts of phosphorus from the Baltic proper that are buried in this subbasin. For the calculation of subbasin budgets, it is crucial where the lateral borders of the subbasins are located, because net transports may change sign with the location of the border. Although the overall transport patterns resemble the results of previous studies, our calculated estimates differ in detail considerably.

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