Chapter 10

Coupled Prediction: Integrating Atmosphere-Wave-Ocean forecasting

CHAPTER
COORDINATORS

John Siddorn

CHAPTER
AUTHORS

Natacha B. Bernier, Øyvind Breivik, Kai H. Christensen, Stephen G. Penny, and Keguang Wang

10.4 Ocean Information Services based on Coupled Frameworks

Over the past decades, operational oceanography underwent a rapid transition and gradually became part of core systems of operational centres previously largely focusing on weather. Sufficient observations are now available to improve the estimation of the ocean state, including mesoscale variability, ice cover, or wave spectra for wave systems. The development of weakly coupled data assimilation techniques, the exploration of strongly coupled data assimilation using cross-domain error covariance (Sluka et al., 2016), the ability to assimilate an ever-growing source of observations, the improvements in physics and dynamics of the various components of the Earth system, and rapidly increasing computing capacities, keep pushing forward the quality of forecasts and reanalyses that can be produced. As a result, information available for products and services is continuously expanding and including a rapid increase in the quality and quantity of ocean and marine services. It is now well established that marine services are essential to any nation with coastal assets.

In the late 90s and early 2000s, operational marine services were limited to a few marine weather variables such as waves, tides, and surges. With coupled systems now in place in many operational centres and the continuous push for increased resolution to better reflect local conditions, a wide variety of new services has and continues to emerge. It is now common for service providers to be overwhelmed with information drawn from many prediction systems, and for users to be submerged with products. In the next subsections are discussed the few steps that should be followed to sort through the very large number of products that can be generated numerically, so that services are centred on needs in a fit for purpose and accessible approach. A few simple examples are used to demonstrate ways of tying together all this numerical knowledge and provide forecasts and services that are informative and tailored to various groups of users.

10.4.1 Establishing service needs

The first step when evaluating services’ needs, including whether to use or not a coupled or forced system, is to clearly define the service gap and how current capacity can be leveraged to address it. The second step is to identify enough resources required to bring the project to completion. Numerous capacities are required to sustain timely and accurate services: i) reliable and sufficient computing resources including telecommunications, bandwidth, and storage, along with staff to operate and maintain the IT infrastructure; ii) physical scientist to install; optimise; run; validate, and verify numerical systems; iii) physical scientists to produce forecasts; iv) forecasters able to disseminate and explain forecasts; v) the ability to sustain such services through extreme conditions (e.g. during a powerful cyclone); and vi) the capacity to overcome throughout the years the changes in IT infrastructure, complexification of systems, increasing volumes of data, etc. However, it should never be forgotten that, whatever is the capacity and the complexity of a state-of-the-art forecast, it only has value if it reaches the users in the due time.

For those countries that choose to operate regional systems driven with data provided by major operational centres, the capacity to download the required data quickly enough to run regional systems and issue timely regional forecasts is also key. It should be also ensured that sufficient local expertise is available to monitor, , and fix any issue with the regional system.

When launching new or improved forecast services, another important step is to identify user groups (e.g. marine engineers, marine transportation industries, search and rescue operations, fisheries and aquaculture, coastal communities) and understand their needs. It should be also kept in mind that within each group there can be considerable modulation of needs and that needs can evolve with time and hence they should be reviewed periodically. See section 4.8 for more details on user requirements.

10.4.2 Identifying the required information

Search and rescue and coastal flooding cases are used to illustrate how to select the modelling tools that are required to best address the problem. They are also used to demonstrate how a fit for purpose approach may identify the numerical systems best suited to deliver services.

A search and rescue incident that requires drift predictions is an example of a service to illustrate the choices needed. Forecasts of the trajectory of the drifting object requires knowledge of tides, eddies, inertial oscillations, winds, and waves. Such incidents often occur during high winds and large waves conditions and, as discussed in 9.1, it is under such conditions that interactions between tides, waves, ocean, and atmosphere are most important. This suggests that coupled predictions could add value (Davidson et al., 2009) to the use of independent ocean, wave, and atmosphere forecasting systems. As already discussed, ensembles are essential to sampling uncertainty in various components of a system. In their comprehensive review of the Deepwater Horizon oil spill event, Barker et al. (2020) made a case for the importance of coupled atmosphere-wave-ocean systems for effective oil spill response. All these considerations point to the use of ensemble coupled ocean-wave-atmosphere systems that are post-processed though tracking systems capable of considering the characteristics of various objects, such as a person in the water or a vessel at drift. However, the simulation overhead (in time and computer resources) of the coupled system needs to be balanced with the need to quickly run ensemble simulations to provide probabilities of the search zone to help optimise search patterns. A case similar to that of search and rescue is the response to oil spill or tracking of nuclear debris, which also requires models to predict particulate dispersion but also need to consider other chemically induced processes, such as fate and behaviour.

Coastal flooding is the other example used here to illustrate how to select the best modelling tools. Local communities typically have precise questions such as: “How much water will there be and for how long?” “Will the water reach my street and my house?” “Will it damage my property?” “Will it erode my land or the cliff my house is perched on?” Local authorities and disaster management agencies might have further considerations such as: “What are the most likely and the worst-case scenarios?” “When should we consider evacuations and through what route?” “What critical infrastructures might be at risk?” However, the nature of the service will depend on local conditions. Consider for example a community living at high latitudes. In the event of a polar low (discussed in 9.1), ice can recede rapidly to expose long stretches of ocean leaving the coastline exposed to large swells. In these areas, wave-ice interactions can lead to rapid changes and coupled ice-ocean-wave-atmosphere systems should be preferred to provide accurate forecasts of the low’s evolution, rapidly changing marine conditions, and to warn the coastal communities. On the other hand, locations exposed to tropical cyclones will need a system more focused on predicting ocean-atmosphere interactions in support of track and intensity prediction. However, the concept of a forecast based on total water level at the coast remains, although the fit for purpose numerical guidance to be used might have some differences. It is then particularly important to consider user orientated questions. User groups rarely care about technical issues, such as if the models are coupled or if the surge component is barotropic or baroclinic. They care that scientists put forward the combination that best addresses their concerns. They want to receive a fit for purpose service. Simulations of tide, surge, wave, erosion, hydrodynamic, and atmospheric may all be required, but to decide whether they should be coupled or not it is necessary to understand if this improves the specific predictions identified by the user questions outlined above.

Advanced knowledge of the risk of an upcoming event is useful to put in place mitigation measures. An outlook for several days to several weeks is of particular interest, as well as the early identification of upcoming risk for which ensemble systems are relevant. At early stages, the focus should be on identifying risk and uncertainties, and communicating them in a clear manner. As the high impact event nears (e.g. next couple days), ensembles can be replaced with resolution increases, so that the risk forecast is changed into an impact-based forecast (i.e. damage to housing, risk of cars being swept away, risk of cutting off of an evacuation route, etc.). This should make the scientists understand that for the users the waves, surges, tides, and other phenomena are relevant only as much as they affect flooding in their areas of interest. This further highlights the importance of metrics used to evaluate models and forecasts. When it comes to flooding, having a slightly better RMSD and thus a better representation of the mean state is useless if the total water level peaks are missed. Thus, relying on an overly complicated ocean system that results in little to no added skill in total water level forecasts is useless. Similarly, if a complicated system cannot be operated with sufficient resolution over long enough periods, or with enough ensemble members to sample uncertainties, it is not fit for purpose. In addition, coupling should be considered also in the context of the resources (always limited) of operational centres.

Finally, whether numerical systems are run locally or remotely and whether all systems required to produce such forecasts are coupled or not, the path forward should be one in which the forecasters are experts at providing added value taking into account the perspective of the public (e.g. placing in the context a particular expected extreme, comparing it to previous ones, explaining the subtle differences to be expected with the forecast risk, etc.). As such, the forecasters are the ultimate downscalers bringing added value based on local knowledge and history

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