Use of real-time observations in an operational ocean data assimilation system: the Mediterranean case

Real-time observations are essential for operational forecasting that in turn can be used to predict changes of the state of the ocean and its associated biochemical fi elds. In addition, real-time observations are useful to detect changes in the past with the shortest delay, to standardize practices in data collection and to exchange data between remote regions of the ocean and seas. Th e drawback is that real-time observations could be less accurate than their delayed mode counterparts due to the time constraints for data dissemination. In situ real-time data are usually decimated to be transmitted in real time (loss of accuracy and resolution), whereas satellite data are corrected with approximate algorithms and less ancillary data. Delayed mode quality control analysis increases the value of the observational data set, fl agging outliers and producing climatological estimates of the state of the system. Th us real-time data, together with a modelling system and the climatological estimates, give the appropriate information for scientifi c studies and applications. Th e principles of operational science started to develop in the 1940s and 1950s, based on the combined use of real-time data and modelling systems that can extend the information from observations in space and time. Operational science is based on a sound knowledge of the dynamics and processes for the space/timescales of interest and operational meteorology and oceanography have started to implement these principles to weather and ocean forecasting activities. In the past 20 years, operational meteorology has become a reality with a network of in situ and satellite observations that has made the weather forecast capable of extending the theoretical limit of predictability of the atmosphere (only one-two days theoretically, now forecasts are useful for more than fi ve days on average). Today meteorological observations are mainly used in their assimilated form even if observations are still collected for specifi c process-oriented studies. Recently the meteorological re-analysis projects (Gibson et al., 1997; Kalnay et al., 1996) have released a wealth of data to be understood and analysed. Th ese data sets are coherent and approximately continuous (daily), fi lling the observational gaps in space and time with a dynamical interpolation scheme. Th e model and the real-time observations are fused in one best estimate of the state of the system by data-assimilation techniques that have been developed to a great degree of sophistication in recent years (Lorenc, 2002). Th e re-analysis data are now forming the basic reference data set to understand climate variability in the atmosphere and upper oceans. Ch20.indd 73Ch20.indd 733 3/7/07 9:58:01 AM Habwatch 734 Dynamical interpolation/extrapolation of observational data for operational forecasting in the ocean began to be investigated at the beginning of the 1980s and the fi rst successful forecasts were carried out in the open ocean (Robinson and Leslie, 1985). Th ese exercises required real-time data that were initially collected with rapid ship surveys realizing adaptive sampling schemes and collecting a combination of traditional recoverable and expendable instruments (CTD, XBTs). At the same time but in a totally independent way, shelf scale and coastal real-time data from moored and drifting sensors such as meteorological buoys and sea-level stations started to be used for shelf scale storm surge operational forecasting (Prandle, 2002). Operational oceanography is now building on this experience and considers real-time measurements from opportunity platforms and satellites in a manner very similar to operational meteorology. Th is chapter aims to show the use of real-time observations in a state-of-the-art ocean-predicting system realized in the Mediterranean. We discuss the pre-processing schemes required to properly assimilate the observations into an operational nowcasting/ forecasting system, elucidate the role and impact of diff erent observations in the assimilation system and show the use of real-time data to evaluate quality of the modelling system. We start with the description of the Mediterranean Forecasting System (MFS) real-time observing system and pre-processing quality control in Section 20.2, we then describe the modelling and assimilation system in relation to the impact of diff erent real-time observations in Section 20.3. In Section 20.4 we evaluate the consistency, quality and accuracy of the forecasting system using model-data intercomparison and Section 20.5 offers conclusion

Forecast and analysis assessment through skill scores

International audienceThis paper describes a first comprehensive evaluation of the quality of the ten days ocean forecasts produced by the Mediterranean ocean Forecasting System (MFS). Once a week ten days forecasts are produced. The forecast starts on Tuesday at noon and the prediction is released on Wednesday morning with less then 24 hr delay. In this work we have considered 22 ten days forecasts produced from the 16 August 2005 to the 10 January 2006. All the statistical scores have been done for the Mediterranean basin and for 13 regions in which the Mediterranean sea has been subdivided. The forecast evaluation is given here in terms of root mean square (rms) values. The main skill score is computed as the root mean square of the difference between forecast and analysis (FA) and forecast and persistence (FP), where the persistence is defined as the average of the day of the analysis corresponding to the first day of the forecast. A second skill score (SSP) is defined as the ratio between rms of FA and FP, giving the percentage of accuracy of the forecast with respect to the persistence (Murphy 1993). The rms of FA is always better than FP and the FP rms error is double than the rms of FA. It is found that in the surface layers the error growth is controlled mainly by the atmospheric forcing inaccuracies while at depth the forecast errors could be due to adjustments of the data assimilation scheme to the data insertion procedure. The predictability limit for our ocean forecast seems to be 5?6 days connected to atmospheric forcing inaccuracies and to the data availability for assimilation

A high resolution free surface model of the Mediterranean Sea

International audienceThis study describes a new model implementation for the Mediterranean Sea which has the presently highest vertical resolution over the Mediterranean basin. The resolution is of 1/16°×1/16° in horizontal and 71 unevenly spaced vertical levels. This model has been developed in the frame of the EU-MFSTEP project and it is the operational forecast model presently used at the basin scale. For the first time in the Mediterranean, the model considers an implicit free surface and this characteristics enhances the model capability to simulate the sea surface height variability. In this study we show the calibration/validation experiments done before and after the model has been used for forecasting. The first experiment consist of six years of a simulation forced by a perpetual year forcing and the other experiment is a simulation from January 1997 to December 2004, forcing the model with 6 h atmospheric forcing fields from ECMWF. For the first time the model Sea Level Anomaly is compared with SLA and with ARGO data to provide evidence of the quality of the simulation. The results show that this model is capable to reproduce most of the variability of the general circulation in the Mediterranean Sea even if some basic model inadequacies stand out and should be corrected in the near future

Forecast and analysis assessment through skill scores

This paper describes a first comprehensive evaluation of the quality of the ten days ocean forecasts produced by the Mediterranean ocean Forecasting System (MFS). Once a week ten days forecasts are produced. The forecast starts on Tuesday at noon and the prediction is released on Wednesday morning with less then 24 hr delay. 5 In this work we have considered 22 ten days forecasts produced from the 16 August 2005 to the 10 January 2006. All the statistical scores have been done for the Mediterranean basin and for 13 regions in which the Mediterranean sea has been subdivided. The forecast evaluation is given here in terms of root mean square (rms) values. 10 The main skill score is computed as the root mean square of the difference between forecast and analysis (FA) and forecast and persistence (FP), where the persistence is defined as the average of the day of the analysis corresponding to the first day of the forecast. A second skill score (SSP) is defined as the ratio between rms of FA and FP, giving the percentage of accuracy of the forecast with respect to the persistence 15 (Murphy 1993). The rms of FA is always better than FP and the FP rms error is double than the rms of FA. It is found that in the surface layers the error growth is controlled mainly by the atmospheric forcing inaccuracies while at depth the forecast errors could be due to adjustments of the data assimilation scheme to the data insertion procedure. The pre20 dictability limit for our ocean forecast seems to be 5–6 days connected to atmospheric forcing inaccuracies and to the data availability for assimilation

A Nested Atlantic-Mediterranean Sea General Circulation Model for Operational Forecasting.

Abstract. A new numerical general circulation ocean model for the Mediterranean Sea has been implemented nested within an Atlantic general circulation model within the framework of the Marine Environment and Security for the European Area project (MERSEA, Desaubies, 2006). A 4-year twin experiment was carried out from January 2004 to December 2007 with two different models to evaluate the impact on the Mediterranean Sea circulation of open lateral boundary conditions in the Atlantic Ocean. One model considers a closed lateral boundary in a large Atlantic box and the other is nested in the same box in a global ocean circulation model. Impact was observed comparing the two simulations with independent observations: ARGO for temperature and salinity profiles and tide gauges and along-track satellite observations for the sea surface height. The improvement in the nested Atlantic-Mediterranean model with respect to the closed one is particularly evident in the salinity characteristics of the Modified Atlantic Water and in the Mediterranean sea level seasonal variability

A nested Atlantic-Mediterranean Sea general circulation model for operational forecasting

A new numerical general circulation ocean model for the Mediterranean Sea has been implemented nested within an Atlantic general circulation model within the framework of the Marine Environment and Security for the European Area project (MERSEA, Desaubies, 2006). A 4- year twin experiment was carried out from January 2004 to December 2007 with two different models to evaluate the impact on the Mediterranean Sea circulation of open lateral boundary conditions in the Atlantic Ocean. One model considers a closed lateral boundary in a large Atlantic box and the other is nested in the same box in a global ocean circulation model. Impact was observed comparing the two simulations with independent observations: ARGO for temperature and salinity profiles and tide gauges and along-track satellite observations for the sea surface height. The improvement in the nested Atlantic-Mediterranean model with respect to the closed one is particularly evident in the salinity characteristics of the Modified Atlantic Water and in the Mediterranean sea level seasonal variability

Daily oceanographic analyses by the Mediterranean basin scale assimilation system

This study presents the upgrade of the Optimal Interpolation scheme used in the basin scale assimilation scheme of the Mediterranean Forecasting System . The modifications include a daily analysis cycle, the assimilation of ARGO float profiles, the implementation of the geostrophic balance in the background error covariance matrix and the initialisation of the analyses. A series of numerical experiments showed that each modification had a positive impact on the accuracy of the analyses: The daily cycle improved the representation of the processes with a relatively high temporal variability, the assimilation of ARGO floats profiles significantly improved the salinity analyses quality, the geostrophically balanced background error covariances improved the accuracy of the surface elevation analyses, and the initialisation removed the barotropic adjustment in the forecast first time steps starting from the analysis

Mediterranean Forecasting System: forecast and analysis assessment through skill scores

Abstract. This paper describes the first evaluation of the quality of the forecast and analyses produced at the basin scale by the Mediterranean ocean Forecasting System (MFS) (http://gnoo.bo.ingv.it/mfs). The system produces short-term ocean forecasts for the following ten days. Analyses are produced weekly using a daily assimilation cycle. The analyses are compared with independent data from buoys, where available, and with the assimilated data before the data are inserted. In this work we have considered 53 ten days forecasts produced from 16 August 2005 to 15 August 2006. The forecast skill is evaluated by means of root mean square error (rmse) differences, bias and anomaly correlations at different depths for temperature and salinity, computing differences between forecast and analysis, analysis and persistence and forecast and persistence. The Skill Score (SS) is defined as the ratio of the rmse of the difference between analysis and forecast and the rmse of the difference between analysis and persistence. The SS shows that at 5 and 30 m the forecast is always better than the persistence, but at 300 m it can be worse than persistence for the first days of the forecast. This result may be related to flow adjustments introduced by the data assimilation scheme. The monthly variability of SS shows that when the system variability is high, the values of SS are higher, therefore the forecast has higher skill than persistence. We give evidence that the error growth in the surface layers is controlled by the atmospheric forcing inaccuracies, while at depth the forecast error can be interpreted as due to the data insertion procedure. The data, both in situ and satellite, are not homogeneously distributed in the basin; therefore, the quality of the analyses may be different in different areas of the basin