Assimilation Of Sea Surface Temperature Data Research Articles

ВЛИЯНИЕ СПОСОБОВ УСВОЕНИЯ СПУТНИКОВЫХ ДАННЫХ O ТЕМПЕРАТУРЕ ПОВЕРХНОСТИ МОРЯ НА ВОСПРОИЗВЕДЕНИЕ ГИДРОФИЗИЧЕСКИХ ПОЛЕЙ ЧЕРНОГО, АЗОВСКОГО И МРАМОРНОГО МОРЕЙ В МОДЕЛИ INMOM

The results are analyzed of the simulation of hydrophysical fields of the Black, Azov, and Marmara seas with the Institute of Numerical Mathematics Ocean Model (INMOM) implemented with a spatial resolution of 4 km, with various assimilation technique for sea surface temperature (SST) data from the SEVIRI sensor installed on MSG satellites. The relaxation (so called nudging) and the ensemble optimal interpolation (EnOI) with various assimilation time frequencies (3-, 6-, 12-, and 24-hour assimilation windows) were used as assimilation methods. It was shown that the assimilation of SST data via the EnOI made it possible to reproduce hydrophysical fields more accurately than via nudging or without assimilation at all. Even with the assimilation of SST data irregularly distributed over space and time, a decrease in the calculation error was observed over the entire sea area, and the structures of the zones of temperature rise or drop were more correctly simulated. The best results were achieved with via the EnOI assimilation with an increasing assimilation frequency over time. When SST data were assimilated using the EnOI, the mean error decreased from 0.16°C (24-hour assimilation window) to 0.08°C (3-hour assimilation window); accordingly, the absolute mean error decreased from 1.03 to 0.33°C, and the standard deviation decreased from 1.33 to 0.42°C. In addition, assimilation using the EnOI 3-hour window improved the reproduction of SST during the period of convection cooling. The assimilation of SST data also led to changes in the structure of the surface sea circulation. In some areas, the direction of currents varied within 5°-10°, and the velocity modulus changed by 3-5%. The assimilation of SST data only slightly reduced errors in the structure of the model vertical temperature profile, which can reach 2°C at depths of 30-40 m. At depths greater than 100 m, deviations did not exceed 0.05°C.

Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System

AbstractSea ice data assimilation can greatly improve forecasts of Arctic sea ice evolution. Many previous sea ice data assimilation studies were conducted without assimilating ocean state variables, even though the sea ice evolution is closely linked to the oceanic conditions, both dynamically and thermodynamically. Based on the method of a localized ensemble error subspace transform Kalman filter, satellite‐retrieved sea ice concentration and sea ice thickness are assimilated into an Arctic sea ice‐ocean model. As a new addition, sea surface temperature (SST) data are also assimilated. The additional assimilation of SST improves not only the simulated ocean temperature in the mixed layer of the ocean substantially but also the accuracy of sea ice edge position, sea ice extent, and sea ice thickness in the marginal sea ice zone. The improvement in the simulated potential temperature in the upper 1,000 m can be attributed to the enhanced vertical convection processes in the regions where the assimilated observational SST is colder than the simulated SST without assimilation. The improvements in the sea ice edge position and sea ice thickness simulations are primarily caused by the SST data assimilation reducing biases in the simulated SST and the associated coupled ocean‐sea ice processes. Our investigation suggests that, due to the complex interaction between the sea ice and ocean, assimilating ocean data should be an indispensable component of numerical polar sea ice forecasting systems.

Correcting the Sea Surface Temperature by Data Assimilation Over the Persian Gulf

In this paper, the impact of sea surface temperature (SST) data assimilation on the results of a finite volume community ocean model has been examined by using the nudging scheme. In this regard, the advanced very high-resolution radiometer satellite SST data were selected as the observational data for assimilation. The numerical modeling was performed over the Persian Gulf from 1998 to 2003 in two different modes: with and without SST data assimilation. The performance of data assimilation with the nudging scheme was evaluated by comparing the simulated SST with the in situ SST measurements and optimum interpolation SST data. Both the spatial and temporal comparisons show the efficiency of assimilation in correcting the model results. The spatial root mean square error in the assimilated run depicts meaningful improvements in the whole of the domain. Also, the temporal comparisons of the results show the capability of assimilation in lowering the model output errors. The simulated SST obtained by applying the data assimilation in the shallow parts of the Persian Gulf matched exactly with the measured ones, especially near the Hormuz Strait. Finally, the results show significant improvements in the SST simulated by using the nudging schemes.

An operational ocean forecast system incorporating NEMO and SST data assimilation for the tidally driven European North-West shelf

A new operational ocean forecast system, the Atlantic Margin Model implementation of the Forecast Ocean Assimilation Model (FOAM-AMM), has been developed for the European North West Shelf (NWS). An overview of the system is presented including shelf specific developments of the physical model, the Nucleus for European Modelling of the Ocean (NEMO), and the Sea Surface Temperature (SST) data assimilation scheme. Initial validation is presented of the tides and model SST. The SST skill of the system is significantly improved by the data assimilation scheme. Finally, an analysis of the seasonal tidal mixing fronts shows that these, in general, agree well with observation, but data assimilation does not significantly alter their positions.

Daily inter-annual simulations of SST and MLD using atmospherically forced OGCMs: Model evaluation in comparison to buoy time series

A systematic methodology for model–data comparisons of sea surface temperature (SST) and mixed layer depth (MLD) is presented using inter-annual simulations from an ocean general circulation model (OGCM), the Naval Research Laboratory (NRL) Layered Ocean Model (NLOM), over 1980–1998. The model–data comparisons performed here are applicable to other OGCMs and are sufficiently detailed to allow easy comparison of results from other models with NLOM, including statistics at specific buoy locations in specific years. There are three model configurations: coarse resolution (1/2°global) and fine resolution (1/8° global and 1/16° Pacific). These are used to assess the sensitivity to OGCM resolution, including the impact of increasing non-determinicity due to flow instabilities in the 1/8° marginally eddy-resolving and 1/1°6 fully eddy-resolving simulations. In addition, sensitivity to the choice of atmospheric forcing product is investigated. For all three models the atmospheric forcing is from the European Centre for Medium-Range Weather Forecasts (ECMWF) during 1980–1998, and for the 1/16° Pacific configuration only the forcing from the Fleet Numerical Meteorology and Oceanography Center (FNMOC) Navy Operational Global Atmospheric Prediction System (NOGAPS) during 1990–1998 is also used. Availability of NOGAPS thermal forcing begins in 1998. Daily averaged buoy time series from the National Data Buoy Center (NDBC) and the Tropical Atmosphere Ocean (TAO) array are used for inter-annual evaluation of these atmospherically forced OGCMs with no assimilation of SST data and no date-specific assimilation of any data type. For the purpose of model evaluation several statistical metrics are calculated comparing buoy and model time series: mean error (ME), root-mean-square difference (RMSD), correlation coefficient ( R), non-dimensional skill score (SS), and normalized RMSD (NRMSD). SST comparisons to the 340 yearlong daily buoy SST time series spanning 1980–1998 gave median values of − 0.09 °C for ME, 0.82 °C for RMSD, 0.92 for R, and 0.73 for SS for the 1/2° global model. Positive SS values, an indication of model success, are found for 286 out of 340 buoys (≈ 84%). An advantage of using a floating mixed layer approach in the global model is demonstrated for simulation of deep and shallow MLD at a buoy location in the Arabian Sea during the 1994–1995 Monsoon period. Model–data comparisons for 1998, when the equatorial Pacific ocean experienced a sharp transition from a strong El Niño to a strong La Niña event, revealed almost no sensitivity to the choice of thermal forcing product. In general, the sensitivity of SST accuracy to model resolution or atmospheric forcing choice was also low based on comparisons to 194 yearlong daily SST time series during 1990–1998. The median RMSD values are 0.68°, 0.61°, 0.84° and 0.84 °C for 1/2°, 1/8°, 1/16° ECMWF-forced simulations and the 1/16° with NOGAPS wind forcing and ECMWF thermal forcing. However, some regional differences exist, e.g., median RMSD values of 0.85 °C for the ECMWF-forced vs. 0.88 °C for the NOGAPS-forced simulations against the 127 TAO buoys, and 0.76 °C for NOGAPS-forced vs. 0.84 °C for ECMWF-forced simulations against the 67 NDBC buoys, all outside the equatorial region. Overall, the results of this paper revealed that 6 hourly wind and thermal forcing products exist that are sufficiently accurate to allow simulated SSTs and MLDs in an OGCM that are typically accurate to within < 1 °C in comparison to daily buoy time series, when there is no assimilation of SST data and when model SST is used in the calculation of latent and sensible heat fluxes. These results indicate that the atmospheric forcing products and the OGCM are suitable for assimilation and forecasting of SST over the global ocean.

Climatological SST and MLD Predictions from a Global Layered Ocean Model with an Embedded Mixed Layer*

The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) with an embedded mixed layer submodel is used to predict the climatological monthly mean sea surface temperature (SST) and surface ocean mixed layer depth (MLD) over the global ocean. The thermodynamic model simulations presented in this paper are performed using six dynamical layers plus the embedded mixed layer at 1/28 resolution in latitude and 0.7031258 in longitude, globally spanning from 728 St o 658N. These model simulations use climatological wind and thermal forcing and include no assimilation of SST or MLD data. To measure the effectiveness of the NLOM mixed layer, the annual mean and seasonal cycle of SST and MLD obtained from the model simulations are compared to those from different climatological datasets at each grid point over the global ocean. Analysis of the global error maps shows that the embedded mixed layer in NLOM gives accurate SST with atmospheric forcing even with no SST relaxation/assimilation. In this case the model gives a global root-mean-square (rms) difference of 0.378C for the annual mean and 0.598C over the seasonal cycle over the global ocean. The mean global correlation coefficient (R) is 0.91 for the seasonal cycle of the SST. NLOM predicts SST with an annual mean error of ,0.58C in most of the North Atlantic and North Pacific Oceans. For the MLD the model gave a global rms difference of 34 m for the annual mean and 63 m over the seasonal cycle over the global ocean in comparison to the NRL MLD climatology (NMLD). The mean global R value is 0.62 for the seasonal cycle of the MLD. Additional model‐data comparisons use climatological monthly mean SST time series from 18 National Oceanic Data Center (NODC) buoys and 11 ocean weather station (OWS) hydrographic locations in the North Pacific Ocean. The median rms difference between the NLOM SSTs and SSTs at these 29 locations is 0.498C for the seasonal cycle. Deepening and shallowing of the MLD at the all OWS locations in the northeast Pacific are captured by the model with an rms difference of ,20 m and an R value of .0.85 for the seasonal cycle. Using several statistical measures and climatologies of SST and MLD we have demonstrated that NLOM with an embedded mixed layer is able to simulate with substantial skill the climatological SST and MLD when using accurate and computationally efficient surface heat flux and solar radiation attenuation parameterizations over the global ocean. Further, this was accomplished using a model with only seven layers in the vertical, including the embedded mixed layer. Success of climatological predictions from the NLOM with an embedded mixed layer is a prerequisite for simulations using interannual atmospheric forcing with high temporal resolution. NLOM gives accurate upper-ocean quantities with atmospheric forcing even with no SST relaxation or assimilation, a strong indication that the model is a good candidate for assimilation of SST data. Finally, the techniques and datasets used here can be applied to evaluation of other ocean models in predicting the SST and MLD.