Aggregating sea surface hydrodynamic forecasts from multi-models for European seas

• Autorzy: Frishfelds V. , She J. , Murawski J. , Nielsen J. W.

Identyfikatory

DOI

10.12716/1001.17.03.04

• Języki publikacji: EN

Abstrakty

EN

Maritime information services supporting European agencies such as the FRONTEX require European‐wide forecast solutions. Following a consistent approach, regional and global forecasts of the sea surface conditions from Copernicus Marine Service and national met‐ocean services are aggregated in space and time to provide a European‐wide forecast service on a common grid for the assistance of Search and Rescue operations. The best regional oceanographic model solutions are selected in regional seas with seamless transition to the global products covering the Atlantic Ocean. The regional forecast models cover the Black Sea, Mediterranean Sea, Baltic Sea, North Sea and combine the North Sea – Baltic Sea at the Danish straits. Two global models have been added to cover the entire model domain, including the regional models. The aggregated product is required to have an update frequency of 4 times a day and a forecasting range of 7 days, which most of the regional models do not provide. Therefore, smooth transition in time, from the shorter timerange, regional forecast models to the global model with longer forecast range are applied. The set of parameter required for Search and Rescue operations include sea surface temperature and currents, waves and winds. The current version of the aggregation method was developed for surface temperature and surface currents but it will be extended to waves in latter stages. The method relies on the calculation of aggregation weights for individual models. For sea surface temperature (SST), near real‐time satellite data at clear‐sky locations for the past days is used to determine the aggregation weights of individual forecast models. A more complicated method is to use a weighted multi‐model ensemble (MME) approach based on best forecast features of individual models and possibly including near real time observations. The developed method explores how satellite observations can be used to assess spatially varying, near real time weights of different forecasts. The results showed that, although a MME based on multiple forecasts only may improve the forecast, if the forecasts are unbiased, it is essential to use observations in the MME approach so that proper weights from different models can be calculated and forecast bias can be corrected. It is also noted that, in some months, e.g., June in Baltic Sea, even SST was assimilated, the forecast still show quite high error. There are also visible difference between different Copernicus Marine Environment Monitoring Service (CMEMS) satellite products, e.g. OSTIA and regional SST products, which can lead different forecast quality if different SST observation products are assimilated.

Słowa kluczowe

EN

weather research and forecasting; search and rescue; operational ocean forecasting; European Satellite Systems; surface current; drifting sea surface temperature; European waters

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2023
• Tom: Vol. 17 No. 3
• Strony: 533--541
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Frishfelds V.

• Danish Meteorological Institute, Copenhagen, Denmark

autor

She J.

• Danish Meteorological Institute, Copenhagen, Denmark

autor

Murawski J.

• Danish Meteorological Institute, Copenhagen, Denmark

autor

Nielsen J. W.

• Danish Meteorological Institute, Copenhagen, Denmark

Bibliografia

• [1] T. N. Krishnamurti, C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, E. Williford, S. Gadgil, and S. Surendran, “Multimodel Ensemble Forecasts for Weather and Seasonal Climate,” J. Climate, vol. 13, pp. 4196–4216, 2000, https://doi.org/10.1175/1520‐0442.
• [2] F. J. Doblas‐Reyes, R. Hagedorn, T. N. Palmer, “The rationale behind the success of multi‐model ensembles in seasonal forecasting—II. Calibration and combination,” Tellus Ser. A‐Dyn. Meteorol. Oceanol.,vol. 57, pp. 234–252, 2005.
• [3] A. P. Weigel, M. A. Liniger, C. Appenzeller, “Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts?” Q. J. R. Meteorol. Soc.,vol. 134, pp. 241–260, 2008.
• [4] T. N. Krishnamurti, V. Kumar, A. Simon, A. Bhardwaj, T. Ghosh, R. Ross, “A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes,” Rev. Geophys., vol. 54, pp. 336–377, 2016.
• [5] I. Golbeck, X. Li, F. Janssen, et al, “Uncertainty estimation for operational ocean forecast products—a multi‐model ensemble for the North Sea and the Baltic Sea,” Ocean Dynamics, vol. 65, pp. 1603–1631, 2015, https://doi.org/10.1007/s10236‐015‐0897‐8.
• [6] D. Bruciaferri, M. Tonani, H. Lewis, J. Siddorn, A. Saulter, J. M. Castillo, N. Garcia Valiente, D. Conley, P. Sykes, I. Ascione, N. McConnell, “The impact of ocean‐ wave coupling on the upper ocean circulation during storm events,” Journal of Geophysical Research, Oceans, vol. 126, issue 6, 2021 https://doi.org/10.1029/2021JC017343.
• [7] E. Clementi, A. Aydogdu, A. C. Goglio, J. Pistoia, R. Escudier, M. Drudi, A. Grandi, A. Mariani, V. Lyubartsev, R. Lecci, S. Cretí, G. Coppini, S. Masina, and N. Pinardi, “Mediterranean Sea Physical Analysis and Forecast (CMEMS MED‐Currents, EAS6 system) (Version 1),” Data set. Copernicus Monitoring Environment Marine Service (CMEMS), 2021.
• [8] E. Jansen, D. Martins, L. Stefanizzi, S. A. Ciliberti, M. Gunduz, M. Ilicak, R. Lecci, S. Cretí, S. Causio, A. Aydoğdu, L. Lima, F. Palermo, E. L. Peneva, G. Coppini,S. Masina, N. Pinardi, A. Palazov, and N. Valchev, “Black Sea Physical Analysis and Forecast (Copernicus Marine Service BS‐Currents, EAS5 system) (Version 1),” Data set. Copernicus Monitoring Environment Marine Service (CMEMS), 2022.
• [9] B. Buongiorno Nardelli, C. Tronconi, A. Pisano, R.Santoleri, “High and Ultra‐High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project”,Rem. Sens. Env., vol. 129, pp. 1‐16, 2013, doi:10.1016/j.rse.2012.10.012.
• [10] J. L. Høyer, P. Le Borgne, and S. Eastwood, “A bias correction method for Arctic satellite sea surface temperature observations,” Remote Sensing of Environment, 2014, https://doi.org/10.1016/j.rse.2013.04.020.
• [11] A. Storto, P. Oddo, A. Cipollone, I. Mirouze, B. Lemieux‐Dudon, “Extending an oceanographic variational scheme to allow for affordable hybrid and four‐dimensional data assimilation,” Ocean Modelling, vol. 128, pp. 67‐86, 2018.
• [12] J. Murawski, J. She, C. Mohn, V. Frishfelds, J. W. Nielsen, “Ocean Circulation Model Applications for the Estuary‐Coastal‐Open Sea Continuum,” Frontiers in Marine Science, vol. 8, 2021.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).