# Implementing operational ocean monitoring and forecasting systems

Pierre de Mey-Frémaux, N.K. Ayoub, Enrique Alvarez Fanjul, Pierre Bahurel, John Wilkin

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Pierre de Mey-Frémaux, N.K. Ayoub, Enrique Alvarez Fanjul, Pierre Bahurel, John Wilkin. Implementing operational ocean monitoring and forecasting systems. 2022, 10.48670/ETOOFS. hal-03798751

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ETOOFS EXPERT TEAM ON OPERATIONAL OCEAN FORECASTING SYSTEMS

IMPLEMENTING OPERATIONAL
OCEAN MONITORING AND
FORECASTING SYSTEMS

EDITORS: ENRIQUE ALVAREZ FANJUL, STEFANIA CILIBERTI, AND PIERRE BAHUREL

GOOS/ETOOFS SECRETARIAT: DENIS CHANG SENG

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unesco

Intergovernmental
Oceanographic
Commission

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MERCATOR
OCEAN

INTERNATIONAL
IOC-UNESCO publication: GOOS-275

June 2022

English only

First edition

The views and opinions expressed in this publication are
those of the authors and do not necessarily represent those
of GOOS, IOC-UNESCO or Mercator Ocean International. The
designations employed and the presentations of material
do not imply the expression of any opinion whatsoever
on the part of GOOS, IOC-UNESCO or Mercator Ocean
International concerning the legal status of any country,
territory, city, or area, or its authorities, or concerning its
frontiers or boundaries.

This publication is available in Open Access under
the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO)
licence (http://creativecommons.org/licenses/by-sa/3.0/
igo/). By using the content of this publication, the users
accept to be bound by the terms of use of the UNESCO
Open Access Repository (http://www.unesco.org/open-
access/terms-use-ccbysa-en). The present licence applies
exclusively to the text content of the publication.

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Editors: Enrique Alvarez Fanjul, Stefania Ciliberti,
and Pierre Bahurel

Editorial Support: Romane Zufic

Cover Design: Abracadabra Estudio de Diseño

Editorial & Graphic Design: Abracadabra Estudio de Diseño

Professional Language Editing: Luca Garibaldi

Corresponding author: Enrique Alvarez Fanjul
ealvarez@mercator-ocean.fr

DOI: https://doi.org/10.48670/ETOOFS

This is an interactive publication, for best experience use
Adobe Acrobat Reader available [here]().

This publication can be referenced as: Implementing
Operational Ocean Monitoring and Forecasting Systems;
IOC-UNESCO, GOOS-275, 2022.

GOOS sponsored by:

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# 1. Introduction

1.1. Scope and purpose of this publication

1.2. References

# 2. Motivation and international context of ocean forecasting

2.1. International cooperation for a sustainable ocean

2.2. International cooperation to build our “common”

2.3. International cooperation to foster openness and inclusiveness

2.4. International frameworks to support OOFS development

2.5. An international community of Operational Ocean Forecasting Systems ready for the next steps

2.6. References

# 3. Definition of ocean forecasting systems: temporal and spatial scales solved by marine modeling systems

3.1. Operational oceanography and ocean forecasting services: definition and main purpose

3.2. Essential ocean variables covered by marine monitoring and forecasting systems

3.3. The spatial scales: downscaling for higher resolutions

3.3.1. Global monitoring and forecasting systems

3.3.2. Regional monitoring and forecasting systems

3.3.3. Coastal monitoring and forecasting systems

3.4. The temporal scales: different applications of numerical modeling to solve ocean problems

3.5. References

# 4. Architecture of ocean monitoring and forecasting systems

4.1. Modelling systems architecture

4.1.1. Step 1 processes

4.1.2. Step 2 processes

4.1.3. Step 3 processes

4.2. Inputs required

4.2.1. Obtaining and preparing ocean data

4.2.2. Description of existing in-situ observational oceanographic data

4.2.3. Description of satellite observational oceanographic data

4.2.4. Bathymetry

4.2.5. Atmospheric forcing

4.2.6. Land forcing

4.2.7. OOFS fields as input for downscaling

4.2.8. Climatology from observations

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4.3. Data Assimilation

4.4. Numerical Ocean models

4.4.1. Definition and types of models

4.4.2. Coupled models

4.5. Validation and Verification

4.5.1. Basis statistical tools for time series validation

4.5.2. Ocean forecasting standard metrics for validation and intercomparison

4.5.3. Qualification, validation and verification processes in support of operational ocean models' production

4.6. Output preparation

4.6.1. Introduction

4.6.2. Products and datasets

4.6.3. Variables

4.6.4. Spatial resolution

4.6.5. Time resolution

4.6.6. Data format

4.6.7. Display and analysis tools

4.6.8. Output dissemination

4.7. User management and outreach

4.8. References

5. Circulation modelling

5.1. General introduction to circulation models

5.1.1. Objective, applications and beneficiaries

5.1.2. Circulation Physics

5.2. Circulation forecast and multi-year systems

5.2.1. Ocean-Earth system as basis for OOFS

5.2.2. Architecture singularities

5.3. Input data

5.4. Modelling component: general circulation models

5.4.1. Mathematical model

5.4.2. Basic discretization techniques

5.4.3. List of Ocean General Circulation Models

5.4.4. Downscaling large-scale solutions to regional/coastal circulation models

5.5. Data assimilation systems

5.5.1. Basic concepts

5.5.2. Sequential methods

5.5.3. Variational methods

5.5.4. Modelling errors

5.5.5. Overview of current data assimilation systems in operational forecasting

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## 5.6. Ensemble modelling

5.6.1. Basic concepts

5.6.2. Ocean model uncertainties

5.6.3. Towards ocean EPS

## 5.7. Validation strategies

### 5.8. Outputs

5.8.1. Variables/EOV

### 5.9. Inventories

5.9.1. Inventory of operational global to regional to coastal to local forecasting systems

5.9.2. Inventory of multi-year systems

## 5.10. References

# 6. Sea Ice modelling

## 6.1. General introduction to sea ice models

6.1.1. Objective, applications and beneficiaries

6.1.2. Fundamental theoretical background

## 6.2. Sea Ice forecast and multi-year systems

6.2.1. Architecture singularities

6.2.2. Input data: available sources and data handling

6.2.3. Modelling component

6.2.4. Ensemble Modelling

6.2.5. Data Assimilation systems

6.2.6. Validation strategies

6.2.7. Outputs

6.2.8. Examples of operational sea ice forecasting systems

## 6.3. References

# 7. Storm surge modelling

## 7.1. General introduction to storm surge

7.1.1. Overview of storm surge disaster

7.1.2. Basic description of storm surge phenomena

7.1.3. Physics of storm surge

## 7.2. Storm surge modelling

7.2.1. Architecture components and singularities

7.2.2. Input data: available sources and data handling

7.2.3. Modelling component

7.2.4. Data assimilation systems

7.2.5. Ensemble modelling

7.2.6. Validation strategies

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7.2.7. Outputs

7.2.8. Existing operational storm surge forecasting systems

7.3. References

8. Wave modelling

8.1. General introduction to wave characterization

8.1.1. Objective, applications, and beneficiaries

8.1.2. General characteristic of waves

8.1.3. Deep water wind-generated wave theory

8.1.4. Nearshore transformation of waves

8.2. Wave forecast and multi-year systems

8.2.1. Architecture singularities

8.3. Input data, available sources, data handling, and model pre-processing

8.3.1. Bathymetry and geometry

8.3.2. Forcing fields

8.3.3. Observations

8.3.4. Pre-processing and definition of the numerical problem

8.3.5. Boundary and initial conditions

8.4. Modelling component: general wave generation and propagation models

8.4.1. Types of models

8.4.2. Discretization methods

8.5. Data assimilation systems

8.6. Ensemble modelling

8.7. Validation and calibration strategies

8.8. Outputs and post processing

8.8.1. Post-processing of the wave model results for the final delivery

8.8.2. Common output variables

8.9. Inventories

8.9.1. Inventory of Near-real time wave forecasting systems

8.9.2. Inventory of Multi-year wave systems (reanalysis, hindcast)

8.10. References

9. Biogeochemical modelling

9.1. General introduction to Biogeochemical models

9.1.1. Objective, applications and beneficiaries

9.1.2. Fundamental theoretical background

9.2. Biogeochemical forecast and multi-year systems

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9.2.1. Architecture singularities

9.2.2. Input data: available sources and data handling

9.2.3. Modelling component

9.2.4. Ensemble modelling

9.2.5. Data assimilation systems

9.2.6. Validation strategies

9.2.7. Output

9.2.8. Higher trophic levels modelling

9.2.9. Inventories

## 9.3. References

# 10. Coupled Prediction: Integrating Atmosphere-Wave-Ocean forecasting

10.1. Introduction to coupled prediction

10.2. Coupling processes

10.3. Benefits expected from coupling

10.4. Ocean Information Services based on Coupled Frameworks

10.5. References

# 11. Downstream applications: From data to products

11.1. The need of downstream products

11.1.1. Blue Economy

11.1.2. Applications and services

11.2. Examples of advanced downstream systems

11.2.1. Sea Situational Awareness (web pages and other dissemination mechanisms)

11.2.2. Oil spill forecasting

11.2.3. Ports

11.2.4. Voyage planning

11.2.5. Fisheries and aquaculture

## 11.3. References

# 12. Challenges and future perspectives in ocean prediction

12.1. Introduction

12.2. Observing system evolution with ocean prediction engagement

12.2.1. Challenges for the current ocean observing systems

12.2.2. Observing System Evaluation

12.2.3. Argo evolution plans

12.2.4. Next phase for satellite missions

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12.3. Numerical models planned evolutions, including adaptation to new HPC systems

12.4. Future evolutions in ocean data assimilation for operational ocean forecasting

12.5. Future of ensemble prediction systems

12.6. Opportunities of artificial intelligence for ocean forecasting systems

12.6.1. Expected contributions of machine learning to ocean forecasting pipelines

12.6.2. Designing fully trainable ocean forecasting systems core engines

12.6.3. Towards user-centric, ocean digital twins leveraging lightweight emulators

12.7. Seamless prediction

12.8. Operational forecasting and scenarios in a digital ocean

12.8.1. Construction of an open ДТО service platform

12.8.2. Underlying architecture

12.9. Quality assessment for intermediate and end users

12.9.1. New observations for improved quality assessment

12.9.2. Expected development of quality assessment techniques

12.9.3. Quality information communication improvements

12.10. Expected future evolution of Copernicus Marine Service products and services

12.11. The United Nations Decade of Ocean Science for Sustainable Development

12.11.1. The Decade Collaborative Centre for Ocean Prediction

12.11.2. CoastPredict Program

12.11.3. ForeSea Program

12.12. References

**Annex 1: Complete list of authors**

**Annex 2: Acronyms**

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# 1. Introduction

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CHAPTER AUTHOR

Enrique Álvarez Fanjul

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01

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05

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# 1. Introduction

## 1.1. Scope and purpose of this publication

## 1.2. References

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## 1.1. Scope and purpose of this publication

It is difficult to overestimate the importance of the ocean for mankind and animal life on our planet. Covering 70% of the Earth's surface, the ocean is the world's largest source of oxygen and absorbs 50 times more carbon dioxide than the atmosphere. Climate is regulated by the ocean heat transport, making our world a habitable place.

For human beings, the ocean is also a source of food, economic resources, travel, and leisure activities. Fish accounts for about 17% of the animal protein consumed globally (FAO, 2020). The economic activities associated with the ocean are many and of crucial importance. For example, the FAO estimates that 59.6 million people in the world are engaged in fisheries activities. Only in the European Union, it is estimated that the blue sector employs almost three and a half million workers. World commerce heavily depends on marine transportation; approximately 50 thousand ships trade internationally, representing 80-90% of world trade (Schnurr and Walker, 2019). About 40% of the world population lives along coasts, which are now endangered by the sea level rise due to climate change and are of paramount importance for economic activity.

These facts and figures highlight that ocean forecasting is considered as a vital activity. The first scientific successful ocean forecasting method was developed during the second world war to facilitate the landings of the US Navy. Swell forecasts were produced by analyzing wind speeds and fetch extension. The first modern approaches, based on the use of numerical models (Pinardi et al., 2017), were developed during the 1950s with the establishment of the basics of storm surge forecasting (Hansen, 1956). Other relevant advances, including first 3-dimensional simulations, took place during the 1960s. Good examples of these achievements can be found on estuarine (Shubinski et al., 1965) and general circulation modeling (McWilliams, 1966).

Since those early successes, thanks to an ever-increasing computing capacity, ocean forecast techniques have evolved to what is today a complex body of codes, data and technologies able to deal with the non-linear and chaotic nature of ocean processes. Today, the scientific modeling community is committed to improve the reliability of the forecasts, mainly advancing on three leading edge areas: data assimilation, coupled forecasting and ensemble modeling.

Advances in computer power have played a relevant key in the progress of the discipline. Figure 1.1 shows the dramatic increase in supercomputing power during the last decades, from the first transistor-based machines to the present day petascale computing.

<figure><img src="image_18.png" /><figcaption>**Figure 1.1.** Supercomputers performance evolution, based on data from www.top500.org site. The logarithmic y-axis shows performance in GFLOPS. Dark blue line: combined performance of 500 largest supercomputers (“Sum”); Light blue line: fastest supercomputer (“Top”); Orange line: supercomputer in 500th place (“#500”) (adapted from 🌐<fnref n="1" />).</figcaption></figure>

The following example illustrates the evolution of ocean forecasting over the last three decades. Figure 1.2 shows the output of two numerical simulations over the same area executed by the same team. The figure on the left was the result of a 1991 state-of-the-art 3D-baroclinic simulation covering the Ria de Vigo. It was the first simulation of this kind in that area, an estuary in the northwest of Spain. The right plot in Figure 1.2 shows a screenshot of the Portus system (Álvarez Fanjul et al., 2018) in the same geographical domain, as generated by an operational system running 3 decades later

The differences between both simulations, summarized in Table 1.1, are striking and demonstrate the great evolution that has taken place in the discipline.

The ocean forecasting discipline includes much more than the execution of the numerical models. There are several steps to transform the data produced into accessible and usable information. The downstream activities to produce tailored ser-

<footnote n="1">1. https://en.wikipedia.org/wiki/History_of_supercomputing</footnote>
<table><caption>**Table 1.1.** State-of-the-art of ocean forecast systems in 1991 and 2021.</caption><thead><tr><th></th><th>1991 Ria de Vigo simulation</th><th>2021 Portus Operational system</th></tr></thead><tbody><tr><th>Type of simulation</th><td>R&D offline simulation</td><td>Fully operational simulation integrated into a service, operated 24/7</td></tr><tr><th>Spatial resolution</th><td>800 meters</td><td>40 meters</td></tr><tr><th>Input</th><td>Climatological wind, rivers and boundary conditions</td><td>Operational, daily update, wind, rivers and boundary conditions</td></tr><tr><th>Execution time</th><td>Several hours of simulation per day</td><td>Minutes of simulation per day</td></tr><tr><th>Storage</th><td>Magnetic tape</td><td>Solid State Disk</td></tr><tr><th>Output</th><td>Mechanical plotter</td><td>Internet, available immediately at global level</td></tr><tr><th>Downstream applications</th><td>No</td><td>Several downstream applications (alert systems, oils spill, etc.)</td></tr></tbody></table>

vices and to serve all kinds of final users are more and more frequent and relevant. This creates a network of scientists, public institutions, private companies, and final users that today is driving the modeling community in a virtuous loop driven by the needs of the users.

The main objective of this guide is to promote the development of new marine forecasting systems around the globe along with the improvement of the existing ones, compiling and making available valuable information to the professionals in charge of developing these services. In writing the

<figure><img src="image_19.png" /><figcaption>**Figure 1.2.** The Ria de Vigo estuarine system as seen by a 1991 simulation (on the left) and the 2021 Portus operational forecasting system (on the right, from 2).</figcaption></figure>

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<footnote n="2">2. https://portus.puertos.es/index.html?locale=en#/</footnote>
various chapters, the authors assumed that the reader has
a general knowledge on ocean science but little or no back-
ground in ocean modeling. The present book intends to pro-
vide an overview of the entire value chain of an Operational
Ocean Monitoring and Forecasting System (OOFS), explaining
its basis. This guide will also focus, although to a lesser extent,
into the more recent technical advances in the sector.

This publication, after the present introductory chapter, has
the following structure:

*   **Chapter 2:** Motivation and international context of ocean forecasting. This chapter describes the activities of the Expert Team on Operational Ocean Forecasting Systems (ETOOFS) and its connections with the World Meteorological Organization (WMO) and the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM). Special focus is made on Copernicus Marine Service and other well-established initiatives. Recent advances in open data policy are introduced, such as the Inspire Open Data Directive. The socio-economic impact of ocean forecasting is described by three pillars: applications, climate and ocean health.

* **Chapter 3:** Definition of ocean forecasting systems: temporal and spatial scales solved by marine modeling systems. This section includes a description of the various temporal and spatial scales involved in ocean modeling, explaining the differences between products derived from large time scales runs (such as re-analysis and scenarios) and forecasting services. It also deals with the significant variations found in the physics and numerical schemes associated with the different spatial scales (global, regional and coastal). This matter is further deepened for each variable in chapters 5 to 9.

* **Chapter 4:** Architecture of ocean monitoring and forecasting systems. It contains a general description of the common aspects to all ocean forecasting systems. It explains the basics related to system architecture (inputs, execution, outputs, etc.), quality control and products. The content of this section works as a template to the topics that will be developed in chapters from 5 to 9.

* **Chapters 5 to 9:** Detailed description of ocean forecasting systems, depending on the variable treated (chapter 5 for Circulation modeling, 6 for Sea-ice, 7 for Sea level and Storm surge modeling, 8 for Wave modeling, 9 for Biogeochemical modeling). The purpose of these chapters is to make understandable, even for non-expert readers, all theoretical and practical basis to properly set-up a forecasting system for a given variable. These chapters provide the physical background on the discipline.

* **Chapter 10:** Coupled prediction: integrated atmosphere-wave-ocean forecasting. In this chapter it is explained the importance of considering the processes described in previous chapters in an integrated way.

* **Chapter 11:** Downstream applications: from data to products. This chapter deals with the way the output data from models are transformed into tailored information for the end user. Examples are made for: web pages and other dissemination mechanisms, oil spill forecasting, alert systems, search and rescue, navigation aid, and fisheries

* **Chapter 12:** Challenges and future perspectives in ocean prediction. Future development and expectations in the field of ocean forecasting are described in the final chapter.

There is no claim of having included in a single monograph all the knowledge and beauty of ocean modeling. This guide hopes to be a guideline and inspiration to professionals all around the globe, stimulating the reader to research deeper knowledge on this vast field. If this objective is achieved, this publication is expected to foster the generation of valuable information that will be used in decision making processes and, therefore, to advocate a wiser and more sustainable relation with our always generous ocean.

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1.2.
References

Álvarez Fanjul, E., Sotillo, M.G., Perez Gomez, B., Valdecasas, M.G., Perez Rubio, S., Lorente, P., Dapena, A.R., Martinez Marco, I., Luna, Y., Padorno, E., Santos Atienza, I., Hernandez, G.D., Lopez Lara, J., Mediaña, R., Grifoll, M., Espino, M., Mestres, M., Cerralbo, P., Sanchez Arcilla, A. (2018.) Operational oceanography at the service of the ports. In "New Frontiers in Operational Oceanography", E. Chassignet, A. Pascual, J. Tintoré, and J. Verron, Eds., GODAE OceanView, 729-736, doi:10.17125/gov2018.ch27.

FAO (2020). The State of World Fisheries and Aquaculture 2020. Sustainability in action. Rome, https://doi.org/10.4060/ca9229en

Hansen, W. (1956). Theorie zur Errechnung des Wasserstandes und der Strömungen in Randmeeren
nebst Anwendungen. *Tellus*, **8**:3, 287-300, https://doi.org/10.3402/tellusa.v8i3.9023

McWilliams, J., C. (1966). Modeling the Oceanic General Circulation. *Annual Review of Fluid Mechanics*, **28**:1, 215-248.

Pinardi, N., Cavaleri, L., Coppini, G., De Mey, P., Fratianni, C., Huthnance, J., Lermusiaux, P.F.J., Navarra, A., Preller, R., Tibaldi, S. (2017). From weather to ocean predictions: an historical viewpoint. *Journal of Marine Research*, **75** (3), 103-159, https://doi.org/10.1357/002224017821836789

Schnurr, Riley E.J., and Walker, T.R. (2019). Marine Transportation and Energy Use. Reference Module in Earth Systems and Environmental Sciences, ISBN 9780124095489, https://doi.org/10.1016/B978-0-12-409548-9.09270-8

Shubinski, R.P., McCarty, J.C., and Lindorf, M.R. (1965). Computer Simulation of Estuarial Networks. *Journal of the Hydraulics Division ASCE*, **92**(3), https://doi.org/10.1061/JYCEAJ.0001470

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2.
Motivation and international
context of ocean forecasting

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CHAPTER AUTHORS

Pierre Bahurel and Enrique Alvarez Fanjul

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## 2. Motivation and international context of ocean forecasting

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2.1. International cooperation for a sustainable ocean

2.2. International cooperation to build our “common”

2.3. International cooperation to foster openness and inclusiveness

2.4. International frameworks to support OOFS development

2.5. An international community of Operational Ocean Forecasting Systems ready for the next steps

2.6. References

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Ocean forecasting took its modern form at the turn of the century, when marine experts on in situ observations, satellite observations, numerical modelling and data assimilation decided to move together towards an integrated approach. Since then, operational oceanography has evolved incredibly fast, fostering communities' engagement, bringing innovations to operations, and structuring new information services for users.

International cooperation was immediately adopted as a
natural framework for the development of ocean forecasting,
and this is still today an indispensable driving force pulling
forward local and national initiatives across continents.

The Global Ocean Data Assimilation Experiment (GODAE),
kicked off in 1997 (Bell et al, 2010), played a leading role cat-
alyzing the initial steps of this revolution by engaging stake-
holders worldwide to build "a global system of observations,
communications, modelling and assimilation, that will de-

liver regular, comprehensive information on the state of the
oceans, in a way that will promote and engender wide utility
and availability of this resource for maximum benefit to the
community". Most of today's ocean forecasting centres were
born to respond to this international call and are directly
built on principles and methods designed in this framework.

GODAE has indeed established the foundation of an interna-
tional cooperation for ocean forecasting and one of its first
outcomes was to build the scientific and technical “common”
required to develop and operate advanced ocean forecast-
ing systems, promoting a cooperation based on openness
and inclusiveness, and driven by constant innovation. This
worldwide activity is benefitting ocean knowledge and is
providing useful tools for decision making actions towards
a more sustainable ocean. These motivations and principles
are still framing and inspiring today our international coop-
eration for ocean forecasting.

2.1.

International cooperation for a sustainable ocean

Operational Ocean Forecasting Systems (OOFS) are amongst
the main and more powerful tools to build the bridge be-
tween marine science and society needs, with a consistent
and state-of-the-art digital depiction of the ocean envi-
ronmental state. It took less than two decades to OOFS to
emerge from science, gain realism and operational maturity,
and convince users of their value; and this is not by chance
if international cooperation was identified from the very first
day as a key condition for success, being today the natural
playground for the development of the OOFS capacity.

Given its importance in the socio-economic and environmen-
tal context, outlined in *chapter 1*, the role of OOFS is gaining
relevance with time, both locally through expert services,
and globally through international coordinated actions. This
importance is being additionally reinforced by the increasing
quality of the forecast services and the international cooper-
ation in data exchange and creation of standards, vital tools
for ocean forecasting.

The three pillars of the Global Ocean Observing System strat-
egy (GOOS, 2020) can help to briefly describe this relevance.
These are applications, climate, and ocean health:

* **Applications (blue economy):** Ocean forecasting is gaining worldwide relevance in creating applied solutions for final users to contribute to a virtuous blue economy. Both public institutions and private companies are taking active steps to implement the so-called "value chain", that transforms ocean observation into information to be employed by end user applications. This is invigorating economic activities, creating jobs, and providing solutions for environmental problems. Chapter 11 provides detailed insight on this particular and presents several relevant applications. Other outstanding set of examples can be found at 🤝<fnref n="1" />.

* **Climate:** Climate change is threatening our ocean and, very particularly, our coasts. For example, sea level rise will produce an increase of coastal erosion and inundation events. And the earth climate cannot be explained without a fair understanding of the ocean climate. The study of climate change scenarios with numerical models is our best tool to assess the hazards, one of the key elements of risk analyses, together with vulnerability and exposure. Ocean global reanalyses produced by ocean forecasting centres as reference simulations

<figure><img src="image_27.png" /></figure>

<footnote n="1">1. https://marine.copernicus.eu/services/use-cases</footnote>
for the past decades are key elements in this domain. These simulations, together with scenario projections, allow us to create climate change impact studies that are the main source of information to design mitigation and adaptation strategies. On shorter time scales, dynamic risk reduction activities are vital for maintaining activities in a changing environment. In this sense, the OOFS will become even more relevant, as the number of extreme events increases. Examples of the ocean climate monitoring by OOFS can be found here, with indicators (☞²) and annual expert reports on the ocean climate (☞³): they are amongst the first OOFS products used by policy makers.

*   **Ocean health:** Human activity is impacting ocean health, increasing its temperature and its acidification. This problem is having very visible and dramatic consequences, such as coral bleaching, increments of harmful algal blooms, migration of species and jellyfish proliferation. Ocean forecasting is a key tool to understand the internal dynamics of these processes and, therefore, provide solutions based on knowledge. Additionally, OOFS are providing vital information for strategic action areas to improve sustainability for future generations; food, energy, tourism transport, energy, and seabed mining, as described by the high-level panel for a sustainable ocean economy (☞⁴). All these activities are benefiting from the accurate forecasts that our present-day systems are providing.

The ocean dynamics is of course not limited by our national boundaries, so international cooperation is necessary when dealing with ocean forecasting. But the other reason for imposing international cooperation to ocean forecasting centres, as a mandatory framework for action, is the need to build a strong community voice supporting the policy effort towards a sustainable ocean. With the Ocean relevance increasingly present on international political agendas (see the UN Agenda 2030, the EU Green Deal, and many other initiatives), knowledge derived from OOFS is today essential, and the only way to achieve this is by action on a global framework. Even when OOFS implementation and ocean applications are local, they contribute to a global challenge. On the local scale they bridge the gap between ocean observations and applications and are active players of a prosperous Blue Economy. But their impact is global, by supporting assessment studies and contributing to an improved local sustainability policy that impacts, at the end, the global ocean.

Due to the previous reasons, the international structura-
tion of the OOFS community is an essential condition for a
sustainable development of the ocean. Ocean Forecasting is
now a recognized player of international ocean governance
fora such as the UN ocean initiatives where ocean policies
are discussed. Remarkably, ocean forecasting has been iden-
tified by the UN as a key contributor for its 14th sustainable
development goal "life below water".

2.2.

International cooperation to build our “common”

All ocean forecasting systems have in common their depen-
dence on reliable ocean observations and state-of-the-art
ocean modelling components, together with the human ex-
pertise and operational processes to design, develop and
operate such systems. Marine services based on OOFS are
consequently critically dependent on the considerable in-
vestments required to develop upstream research for ca-
cacity building and large infrastructures such as satellites,
marine observation networks and super-computers. Such

massive investments are not affordable at the scale of one
entity or one nation, and international cooperation is the
only framework able to ensure a sustainable effort in this
matter and build this indispensable framework for the OOFS
worldwide community.

The “butterfly diagram” (Figure 2.1) illustrates the position of
ocean forecasting in the overall value chain, bridging ocean
observations with end-users (Bahurel et al., 2010). Ocean
forecasting is present on the two steps signed with gears
on the figure. On one hand, at the so-called “core services”,
where its mission consists in integrating the richness and
variety of ocean observations to build a state-of-the-art
description of the ocean environment, multi-variable, con-
sistent in space and time, reliable, and immediately action-

2. https://marine.copernicus.eu/access-data/ocean-monitoring-indicators
3. https://marine.copernicus.eu/access-data/ocean-state-report
4. https://www.oceanpanel.org/

<figure><img src="image_28.png" /></figure>
<figure><img src="image_29.png" /><figcaption>**Figure 2.1.** From observation to end user services: the ocean value chain.</figcaption></figure>

able by expert services in their own fields of expertise. On a second step, intermediate users can use this freely available description to enrich the data via model downscaling and, finally, generate tailored information and indicators for decision making. This is done via a large variety of downstream services, such as storm surge forecast warning systems or water quality monitoring services.

The left wing of this butterfly (Marine Core Service) also illustrates the OOFS 'common' – i.e., the assets developed through international cooperation and shared by the OOFS community as a common good – that the GODAE initiative has invented two decades ago, and that international cooperation has developed through different channels.

This OOFS 'common' includes:

* **Data:** Ocean observations measured from space by satellites, and in situ by vessels and autonomous networks as well as different forcings or coupling with atmosphere and river run-off required to run OOFS systems. International cooperation has not only organised and simplified the access to these data worldwide, but also has strengthened the voice of the OOFS community for a sustained observation effort.

* **Tools:** Operational tools used to generate a reliable 4-dimensional description of the ocean environment and operate ocean forecasting systems include models, data assimilation systems and product generation software. Full-fledge integrated systems and their parameterization are specific to their purpose, location, and operating team but they are composed of individual bricks (e.g., NEMO -Nucleus for European Modelling of the Ocean- modelling tool) that are frequently shared to feed the OOFS common.

* **Standards:** data sharing or product validation are driven by expert processes and standards to reach efficiency to interoperability. One of the best examples is the adoption of common data formats to facilitate exchanges. The international community work started with GODAE in the field of product validation with the definition of common protocols ("metrics") now widely adopted by OOF centres worldwide.

* **Products:** together with input data entering OOFS, the output products generated by these systems (ocean forecasts, ocean simulations, ocean indicators, ...) are widely shared to feed a common set, facilitating intercomparison and individual systems improvements.

* **Know-how:** the outcomes of research undertaken by scientists in the OOFS fields are of course a key asset for the community worldwide, and the scientific community has proposed in this matter fruitful models of cooperation; this common knowledge has been extending its scope with the development of OOFS through the sharing of best practices in the domain of system operations, market development and user uptake.

This is at the heart of international cooperation and its content is in constant evolution. It reveals the strength and the dynamism of the OOFS community: we are prepared to see in the coming years a bloom of innovation to support OOFS development in the coastal zones, marine biodiversity, polar areas, climate adaptation, all integrated in the new paradigm offered by digital twinning.

<figure><img src="image_30.png" /></figure>
2.3.

International cooperation to foster openness and inclusiveness

The principle of openness - free and open sharing of data, exchange of knowledge, interdisciplinary cooperation - and the unwavering ambition to generate at every moment the best possible information to improve our knowledge on the ocean and contribute to a sustainable development are fundamentals in this approach. This principle is a cornerstone of international cooperation and has been a key factor of success of the development of ocean forecasting.

One of the most structuring elements has been the adoption
of open & free data policy amongst our community. As previ-
ously mentioned, data sharing is a vital need to have a pre-
dicted ocean. The Resolution 40 of the World Meteorological
Organization related to the exchange of meteorological data
was the first model identified by weather oceanographers.
Afterwards, the scope has been extended by several interna-
tional initiatives developed to promote data exchange and
systems interoperability at a wider scale, facilitating inter-
disciplinary approaches. For example, the INSPIRE Directive
aims to create a European Union spatial data infrastructure
for the purposes of EU environmental policies and activities
which may have an impact on the environment (☞ 5).

This European Spatial Data Infrastructure is enabling the sharing of environmental spatial information among public sector organisations, facilitating its public access across Europe, and assisting in policymaking across boundaries. Beyond its simplicity for implementation (open sharing means less energy spent in control and more in value creation), openness is a key condition for inclusiveness. Being inclusive is identified as particularly important in the field of modern oceanography where stakeholders, motivations and situations are particularly diverse and rich. The Copernicus Marine service in Europe has shown the strength of a state-of-the-art operational service implemented by hundreds of experts and teams, distributed throughout Europe, coming from public and private sectors, from operational and research organisations, from different countries, from diverse cultures and relations to the ocean. Openness facilitates inclusiveness and enables diversity, bringing together the best skills and fostering capacity building. This principle of inclusiveness is particularly important to successfully manage the seamless integration of coastal centres in the OOFS framework, where – here again – the first priority will be to build a worldwide capacity open and benefitting to all.

2.4.

International frameworks to support OOFS development

Amongst the United Nations framework, the Intergovern-
mental Oceanographic Commission (IOC) of UNESCO is
critically instrumental to build the world ocean ‘basic
infrastructure’, i.e., observations, data management, and
forecasting. Within IOC, the Global Ocean Observing Sys-
tem (GOOS) program is a key element of this communi-
ty effort, and a powerful instrument to structure expert
cooperation in the different related thematic areas (e.g.,
from physics to biology) and the different regions through
the GOOS Regional Alliances. In this frame, the Expert Team
on Operational Ocean Forecasting Systems (see below) is
fully devoted to ocean forecasting. The IOC Internation-
al Oceanographic Data and information Exchange (IODE)

program complements this effort, with special focus on
the setting of ocean data information system.

Beyond IOC-UNESCO, the other GOOS sponsors – World Meteorological Organization (WMO), UN Environment Program (UNEP) and the International Science Council (ISC) – are all active players in this area. WMO offers a solid model of organisation, set for weather forecasting, where the management of basic infrastructures on one hand – see for instance the WMO Global Data Processing and Forecasting System (GDPFS) that coordinates Member capacities to prepare and make meteorological analyses and forecast products – and the management of weather services on the other, can inspire the world ocean community and propose immediate hooks to develop ocean/weather synergies, as it is already

<figure><img src="image_31.png" /></figure>

<footnote n="5">5. https://inspire.ec.europa.eu/</footnote>
the case for observations. The marine component of Global Environment Monitoring System (GEMS Ocean) of UNEP is another example where ocean prediction information is identified as a key source of information. As an illustration, we can observe how the World Environment Situation Room – which is an environmental dashboard operated by UNEP for its Member States – includes in its Ocean/SDG14 section operational ocean prediction products.

It is in this framework where the ETOOFS (Expert Team on Operational Ocean Forecasting Systems) action takes place: hosted by IOC and co-sponsored as GOOS by IOC, WMO, UNEP and ISC, this body brings together experts representing each continent, highly motivated to share and improve their experience and skills to help developing countries build their national centres for operational oceanography. ETOOFS enables worldwide use of timely and reliable ocean forecasts for applications in national security, environmental protection, and the maritime economy. It is a vital operational link between observing networks and marine services.

Amongst others, the following ETOOFS activities are to be highlighted:

* Manage and maintain guide, scope and requirement documents for countries providing ocean forecasting services.

* Manage and maintain an overview of active operational ocean forecasting systems.

* Manage and promote the adoption of an international standard to support interoperability and common formatting of ocean forecast products and services.

* Guide and initiate actions contributing to improving operational ocean prediction system efficiency, fidelity and service quality.

* Promote and facilitate support for, and development of, operational and forecasting systems and their adoption in the wider community.

* Provide advice on operational ocean forecasting systems related matters and prepare submissions on the requirements of operational ocean forecasting systems operated by countries to other international groups.

Multilateral initiatives such as the G7 Future of Seas and Ocean Initiative ( OceanPredict<fnref n="6" />) or the Blue Planet component of the international GEO (Group on Earth Observations) program ( OceanPredict<fnref n="7" />) are other relevant frameworks where the value of OOFS is

progressively recognized and further developed. We observe for instance how the priorities set by the G7 FSOI (Future of the Seas and Oceans Initiative) on digital oceanography (amongst others) and by GEO Blue Planet on marine applications are dependent on ocean prediction.

It is also important to mention the tremendous contribution of large organisations such as the European Commission in Europe, which, in the frame of its space program Copernicus, has created and supports a unique Copernicus Marine forecasting service (Le Traon et al., 2017) organisation ( OceanPredict<fnref n="8" />). This European-made service follows the international cooperation principles presented above, with a global impact as the first driver, and was the key to design, build and operate a “core” service following common good principles, and to implement a full open data policy and an inclusive service organisation across Europe. The link with the other continents – America, Asia, Australia, Africa – is at the heart of Copernicus Marine. Similarly, we can observe how the African Union has encouraged structuring initiatives for a prosperous Blue Economy across the African continent with IOC, with GEO and other programs where ocean forecasting is instrumental. The workshop organised by ETOOFS in the preparation phase of the present guide has shown a remarkable demand on all continents for a reinforced community approach and OOFS capacity development worldwide.

OceanPredict (OceanPredict<fnref n="9" />), which descends directly from the initial GODAE initiative, is the best international framework to develop science & technology initiatives in the field of ocean forecasting. At a scientific level, the modelling community is indeed self-organised thanks to it. This is a team dedicated to work with the Global Ocean Observing System (GOOS) and associated groups to co-design and co-develop the ocean observing and forecasting system of the future, with the aim of delivering the essential information needed for safety, wellbeing, and prosperity. OceanPredict is a vigorous and strong international coordination mechanism to build the ocean prediction capacity of the future. This will be achieved thanks to the improvement of the science, capacity, efficacy, use, and impact of ocean prediction systems, contributing to a seamless ocean information value-chain, from observations to end users, for economic and societal benefit.

Finally, the launch by IOC-UNESCO of a “UN Decade of Ocean Science for Sustainable Development” is an excellent opportunity to do more, reinforce international cooperation and enrich our community and knowledge in the field of ocean prediction. The relevance of ocean forecasting systems will be even larger in the future, with more reliable

<figure><img src="image_32.png" /></figure>

<footnote n="6">6. https://www.g7fsoi.org/</footnote>

<footnote n="7">7. https://geoblueplanet.org/</footnote>

<footnote n="8">8. https://marine.copernicus.eu/</footnote>

<footnote n="9">9. https://oceanpredict.org/</footnote>
and interoperable services, able to serve a wider range of
final users. In this process, the activities of the UN Decade
of Ocean Science will be of paramount importance (UNES-
CO-IOC, 2021). Under the vision “the science we need for the
ocean we want”, the Ocean Decade will implement trans-
formative ocean science solutions for sustainable devel-
opment. The following outcomes describe “the Ocean We
Want, which is the aim and final target of this initiative:

* A clean ocean where sources of pollution are identified and reduced or removed.

* A healthy and resilient ocean where marine ecosystems are understood, protected, restored and managed.

* A productive ocean supporting sustainable food supply and a sustainable ocean economy.

* A predicted ocean where society understands and can respond to changing ocean conditions.

* A safe ocean where life and livelihoods are protected from ocean-related hazards.

* An accessible ocean with open and equitable access to data, information, technology, and innovation.

* An inspiring and engaging ocean where society understands and values the ocean in relation to human wellbeing and sustainable development.

As a part of the Decade activities, Mercator Ocean International will implement a Decade Collaborative Center for Ocean Prediction (OceanPrediction DCC). This initiative will provide: i) a global forum to focus and optimise the efforts of individual Decade programmes on achieving the collective goals of the Decade, ii) a communication and collaboration hub that brings together Decade programmes with ocean prediction activities, institutes, and organisations outside of the Decade, and iii) the global technical and organisational structure required to establish a pilot for a Global Ocean Data Processing, Modelling, and Forecasting System building on the innovations generated by Decade programmes – such as CoastPredict, Foresea or DITTO (Digital Twins of the Ocean) to name a few – and other national, regional, international and intergovernmental partners.

2.5.

An international community of Operational Ocean Forecasting
Systems ready for the next steps

The OceanPredict website describes a first series of ocean for-
casting systems, projects and centres, illustrating the worldwide
dynamism of this scientific and operational domain: they are
BlueLink in Australia, Concepts in Canada, ECCO, Hycom and
NCEP in the United States, ECMWF and the Met Office in the
United Kingdom, INCOIS in India, Mercator Ocean International
in France, CMCC (Centro Euro Mediterraneo sui Cambiamenti Cli-
matici) in Italy, MOVE/MRI in Japan, NMEFC (National Marine En-
vironmental Forecasting Center) in China, REMO in Brazil and the
NERSC (Nansen Environmental and Remote Sensing Center) in
Norway: they have in common their global and basin-scale geo-
graphical extensions and also their international visibility. But
how many other OOFS could we map all over the world? What
about SAMOA (Sistemas de Apoyo Meteorológico y Oceanográf-
ico de las Autoridades Portuarias) in Spain (Alvarez Fanjul et al.,
2018)? How many of them are in the Pacific or along the African
coast? In the Mediterranean only, and for the currents only, we
can find 32 different forecasting systems according to the Mon-

GOOS webpage ( 👤10): they can be local, they can have different
purposes, missions or maturities but they are exemplary of the
richness and readiness of the OOFS community worldwide. It is
time for us all to structure further this talented community and
make it visible. The new international momentum offered by the
UN Decade of Ocean Science on one hand and the technological
breaks proposed by the integration of digital twining is a real
chance. Ocean prediction centres are ready for a new step in
this digital oceanography, and they are well prepared: commit-
ted for a sustainable ocean, for a state-of-the-art common set
of assets nourished by all and benefitting to all, and for an open
and inclusive approach.

10. http://www.mongodb.eu/

<figure><img src="image_33.png" /></figure>
## 2.6. References

Alvarez Fanjul, E., Sotillo Garcia, M., Perez, B., Garcia Valdecasas, J.M., Perez Rubio, S., Lorente, P., Dapena, A.R., Martinez, M., Luna, Y., Padorno, E., Santos Atienza, I., Diaz Hernandez, G., Lara, J.L., Medina, R., Grifoll, M., Espino, M., Mestres, M., Cerralbo, P., Sanchez Arcilla, A. (2018). Operational oceanography at the service of the ports. In: "New Frontiers in Operational Oceanography". E. Chassignet, A. Pascual, J. Tintoré, and J. Verron, Eds., GODAE Oceanview, 729-736, doi:10.17125/gov2018.ch27

Bahurel, P., Adragna, F., Bell, M., Jacq, F., Johannessen, J., Le Traon, P.-Y., Pinardi, N., She, J. (2010). Ocean Monitoring and Forecasting Core Services, the European MyOcean Example. Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society, doi:10.5270/OceanObs09.pp.02

Bell, M., Lefebvre, M., Le Traon, P.-Y., Smith, N., Wilmer-Becker, K. (2009). GODAE: The global ocean data assimilation experiment. Oceanography, 22(3), 14-21, https://doi.org/10.5670/oceanog.2009.62

GOOS. (2020). A Roadmap for the Implementation of the Global Ocean Observing System 2030 Strategy. IOC, Paris, GOOS Report No. 249.

Le Traon et al. (2017). The Copernicus marine environmental monitoring service: main scientific achievements and future prospects. Special Issue Mercator Océan International #56. Available at: https://marine.copernicus.eu/it/node/594

UNESCO-IOC (2021). The United Nations Decade of Ocean Science for Sustainable Development (2021-2030) Implementation Plan. UNESCO, Paris (IOC Ocean Decade Series, 20).

<figure><img src="image_34.png" /></figure>
3.
# Definition of ocean forecasting
systems: temporal and spatial scales
solved by marine modeling systems

<figure><img src="image_35.png" /></figure>

CHAPTER COORDINATOR

Enrique Alvarez Fanjul

CHAPTER AUTHORS (in alphabetical order)

Marcos Garcia Sotillo and John Wilkin

<figure><img src="image_36.png" /></figure>

01

02

03

04

05

06

07

08

09

10

11

12
## 3. Definition of ocean forecasting systems: temporal and spatial scales solved by marine modeling systems

3.1. Operational oceanography and ocean forecasting services: definition and main purpose

3.2. Essential ocean variables covered by marine monitoring and forecasting systems

3.3. The spatial scales: downscaling for higher resolutions

3.3.1. Global monitoring and forecasting systems

3.3.2. Regional monitoring and forecasting systems

3.3.3. Coastal monitoring and forecasting systems

3.4. The temporal scales: different applications of numerical modeling to solve ocean problems

3.5. References

<figure><img src="image_37.png" /></figure>
# Operational oceanography and ocean forecasting services: definition and main purpose

Operational Oceanography is defined as the set of activities for the generation of products and services providing information on the marine and coastal environment. OO is designed to meet different societal, economical, scientific and other user needs. As defined by the EuroGOOS, there are two main pillars in OO services: i) the monitoring element, which focuses on the systematic and long-term routine measurements of oceans and atmosphere, and their rapid interpretation and dissemination; and ii) the prediction component, which uses ocean models to generate a variety of products that may be nowcasts (the most accurate description of the present ocean state provided by the analyses), forecasts (the future condition of the ocean for as far ahead as possible) or hindcasts (the most complete description of past states, provided by reanalysis).

Understanding the physical behavior of ocean and coastal areas provides an important guidance to manage issues related to anthropic impacts and resource exploitation activities. A wide variety of operational ocean models have been and are currently used to tackle different issues and to support various service purposes. These different types of ocean model applications, specific for each problem to be solved, are based on different computer codes and parameterizations. They resolve a range of spatial and temporal scales (with different model resolutions) using a miscellany of data sources (as forcing initial and boundary conditions) and can rely or not on data assimilation methods to integrate observations (Schiller et al., 2018).

Wind, waves and sea-level traditionally were the most important met-ocean parameters for maritime activities due to their implications for marine safety and impacts on operations and navigation conditions. Therefore, these parameters have been the most extensively monitored and forecasted since earlier times and their forecasting has frequently been the responsibility of meteorological services. The traditionally strong connection between waves and weather prediction is reinforced by the direct interaction between waves and winds, which makes the waves a special case with specific models coupled only with atmospheric models (see Chapter 10), resulting in a separated development of ocean and wave models. Nevertheless, in the last decade the gap between ocean and wave models is diminishing and they are being progressively integrated in more comprehensive operational ocean coupled systems (in some cases also coupling with the atmosphere).

The sea level is the other key variable that counts with a long tradition in operational services based on specific models.

Sea level prediction services have supported very different human activities, mostly related to navigation in shallow waters being harbors, estuaries and other coastal areas impacted by tides and appreciably sub-tidal variability. Sea level forecasting of storm surge is a key element in coastal flooding warning systems. Originally, only astronomical tidal predictions were used in the sea level forecasting but progressively this approach was augmented by the use of storm surge models, which are based on single-layer homogeneous density barotropic ocean models but include also very detailed bathymetries with astronomical tidal forcing and a meteorological residual contribution (see Chapter 7). Currently, storm surge forecasting is also benefiting from the sea surface height products delivered by the available high-resolution 3D global and regional baroclinic models operated by different ocean forecasting services (Pérez et al., 2012).

A recent overview of the current European capacity in terms of operational modeling of marine and coastal systems (Capet et al., 2020) provides a comprehensive panorama of what are the essential ocean variables and phenomena of most interest in relation to their relevance for regional environmental issues and their impact on different economic sectors. An interesting output from the survey performed to underpin this study reveals that nowadays a vast majority of the identified OO forecast services operate hydrodynamic models (see info on them in Chapter 5), with waves and biogeochemical models (see Chapters 8 and 9) also represented but to a lesser extent. Other specific models, such as for particle drift prediction and sea ice (see Chapter 6), are scarcer in the operational landscape. The study also reveals how currents, salinity, temperature, and sea surface height are resolved for almost all operational models. Instead, basic variables of biogeochemistry (e.g., oxygen, nutrients, phyto- and zooplankton biomasses, suspended, and organic matter) are much less represented in the ocean forecasting services. To date, marine safety, oil spills and sea level monitoring appear as the phenomena mostly addressed by European operational models (with more than 40 implementations). Storm surges, water quality, and eutrophication are well-considered at present (~ 15-25 implementations) and will benefit from an extended coverage in the coming years (~ +30-50 % within 5 years). Finally, it must be pointed out that harmful algal blooms, shoreline/bathymetry changes, and ocean acidification receive some attention but remain limited in their coverage.

Biogeochemical models have a greater complexity, as they involve many more state variables, parameters, uncertain

<figure><img src="image_38.png" /></figure>
processes, interactions and drivers, which means that they
may not have yet reached the level of maturity required for
accurate simulations and useful outputs; for these reasons
their adoption in operational applications is presently limit-
ed. This also applies to the use of data assimilation in coast-
al operational application or sea ice coupled models, even
though in the past decade substantial efforts have been
dedicated to developing and improving comprehensive glob-
al and regional operational forecasting services. An example
is the case of the service delivered by the marine component
of the Copernicus Program of the European Union (Coperni-

cus Marine Service, 2021a) which provides free, regular and
systematic information on the state of the Blue (physical in-
cluding waves), White (sea ice) and Green (biogeochemical)
ocean at global and regional scales, on the basis of model
applications with the appropriate complexity suitable for
operational forecasting. Finally, it is to be noted that sus-
tained availability of global and regional scale core products,
such as the ones delivered by Copernicus Marine Service, has
fostered the development of specific "downstream" services
devoted to coastal forecasting, favoring synergies between
different existing services (Sotillo et al., 2021).

## 3.2.
# Essential ocean variables covered by marine monitoring and forecasting systems

Numerical ocean models generate as output a substantial number of variables and volume of data. The variety of variables dealt by such models depends on the type of model applied, the processes included, and the systems involved (for example, ocean models can be coupled to atmospheric and surface wave models, as well as to sea ice models or biogeochemical ones). On the monitoring side, the ability to measure the ocean with new technologies and techniques (related to both remote-sensed and in-situ device observations) has been continuously enhanced since the 1980s as well, resulting in an extended range of ocean variables to deal with.

This wide variety in terms of variables used to monitor and
model the ocean is reflected by the CF metadata conventions
(1'). These conventions are intended to be used with cli-
mate and forecast data derived from atmosphere, surface
and ocean models, and from comparable observational
datasets, and are designed to facilitate the processing and
sharing of data files via widely used formats (e.g. NetCDF
and ZARR) and web services (e.g. THREDDS and ERDDAP);
their use is supported by a wide range of software. The CF
Standard Names Table (2) is a living document that is con-
tinuously expanded following requests for new variables. In
its version 77, dated 19 January 2021, there were 579 stan-
dard names that match a query for the strings "seawater" or
"ocean". This number gives an idea of the broad panorama
existing in terms of ocean climate and forecast variables.

Due to this great breadth and differentiation of ocean vari-
ables, the need arose to agree on some common key vari-
ables to monitor the ocean. In the late 1990s, in part motivat-
ed by requirements to support activities and negotiations in
the framework of the UNFCCC and the IPCC, emerged the con-
cept of ECVs. An ECV is a physical, chemical or biological vari-
able (or a group of linked variables) that critically contrib-
utes to the characterization of Earth's climate. Furthermore,
the ECV datasets progressively became also widely used in
the context of mitigation and adaptation measures, as well
as to assess risks and enable attribution of climate events to
underlie causes. This is the fundamental importance of ECVs
and the reason for which climate services focus resources
to monitor and forecast these minimal sets of "key vari-
ables". Currently, there are 54 identified ECVs (GCOS, 2021).
Global expert panels, coordinated by GCOS, are responsible
for maintaining updated definitions of the ECVs required to
systematically observe the Earth's changing climate. The ECV
specification sheets are intended to be observation platform
agnostic, not focusing on what any given existing or novel
observational technology can deliver, but on the ultimate
resolution and accuracy that the full network of coordinated
systems can achieve to meet user requirements.

The WMO defines the following ECVs specifically focused on
the ocean (WMO, 2021):

* 12 related to physics: Ocean Surface Heat Flux, Sea Ice (including Concentration, Extent/Edge, Thickness and Drift), Sea Level (Global Mean and Regional), Sea State (Wave Height), Surface Stress, Temperature, Salinity and Currents for both Sea Surface and Subsurface;

<figure><img src="image_39.png" /></figure>

<footnote n="1">1. http://cfconventions.org/latest.html</footnote>

<footnote n="2">2. http://cfconventions.org/standard-names</footnote>
<figure><img src="image_40.png" /><figcaption>**Figure 3.1.** Essential Ocean Variables (EOVs): lists of parameters delivered by the Copernicus Marine Service for the physical ocean (including sea wave state), biogeochemistry and sea ice.</figcaption></figure>

* 6 biogeochemical: Inorganic Carbon, Nitrous Oxide (including interior ocean N2O and N2O air-sea flux), Nutrients (including ocean concentrations of silicate, phosphate and nitrate), Ocean Colour (Chlorophyll-a Concentration), Oxygen, and Transient Tracers (CFCs, etc.).

* 2 Biological/Ecosystems: Marine Habitat Properties (Coral Reefs; Mangrove Forests, Seagrass Beds, Macroalgal Communities) and Plankton (Phytoplankton and Zooplankton).

Ocean monitoring and forecasting services focus their resources on covering most of these ocean ECVs. Actually, there is an expanded list of EOVs maintained by the GOOS (Sloyan et al., 2019) in collaboration with panels provided by the OOPC panel and the IOCCP. GOOS aims to periodically re-evaluate and update the EOVs list. Importantly, the EOVs include observable ecosystem and biogeochemical characteristics of the ocean that are needed for understanding the state and health of the marine environment, currently under pressure by human stressors and climate change. While networks that observe the physical ECVs/EOVs are generally well established, those working on biogeochemical and ecosystem EOVs are in most cases still in the concept or pilot phase. Nevertheless, acquisition of these data by regional and global observing systems is essential to the development of model-based forecasting capabilities. For further details on the on-going actions and

the path forward to extend operational monitoring of these ocean variables see Muller-Karger et al. (2018).

Some marine services already go beyond the Ocean-ECV and EOV lists, delivering model and observation products for a broader set of variables. This is the case for the Copernicus Marine Service, which monitors Ocean-ECVs (as described in its "Ocean State Report (OSR)", Copernicus Marine Service, 2021b), but goes even further than the common list delivering more variables and indicators of interest for a wide-ranging end user community through its Product Portfolio (Copernicus Marine Service, 2021c). A summary of EOVs and parameters delivered by the Copernicus Marine Service is shown in Figure 3.1. Copernicus Marine Service is a good example of what occurs across most of the trans-national, national or regional ocean monitoring and forecasting services.

<figure><img src="image_41.png" /></figure>
3.3.

The spatial scales: downscaling for higher resolutions

Ocean dynamics are described through equations of motion (the Navier–Stokes equations) that are well established for ocean physics (mass, momentum and heat). However, these equations formally apply to the continuum level, whereas in ocean forecasting they are solved on a computational grid with a finite number of cells and discrete resolution. Fur- thermore, in virtually all computational environmental fluid dynamics fields approximations are made to the governing equations to make their solution tractable. The trade-offs between model resolution, ocean dynamical processes re- solved, and computational effort are discussed elegantly by Fox-Kemper (2018). At the outset, ocean modelers are con- fronted with the key decision of choosing the appropriate spatial resolution for each specific ocean model application. In global operational model applications, relatively coarse resolutions of the order 10s to 100s kilometers are common, whereas in coastal models far higher spatial resolution is needed (perhaps as little as hundreds of meters). The choice of spatial resolution inevitably sacrifices sub-grid-scale dy- namical processes that are impractical to explicitly resolve and must instead be somehow parameterized.

Over the past 30 years there has been a steady evolution in
ocean model resolution, in a direct proportion with enhance-
ments and availability of computing resources. This resulted
in a finer spatial resolution that allowed significant improve-
ment in the simulation of oceanic flows. A major milestone
in the evolution of ocean modeling was the introduction of
eddy resolving models. This class of models, with spatial res-
olution (less than 1/4° in latitude and longitude; or around 25
km) sufficient to allow the spontaneous emergence of ocean
mesoscale eddies, was a major ocean model achievement,
improving the quality of global simulations and opening the
door to accurate regional ocean modeling. However, as de-
scribed in the next section, this resolution is now eclipsed in
global operational systems.

The continuous increase of resolution along with the pro-
gressive enhancement of models was also due to the more
explicit inclusion of higher frequency processes, such as the
representation of tidal motions and the better representation
of turbulence and mixing processes in shallow waters. These
improvements have pushed the use of ocean models into the
mesoscale resolved and sub-mesoscale-permitting regime,
allowing their uses also for coastal purposes. As a result of
this progressive evolution of ocean modeling, we have today
an ocean model landscape composed of global, regional and
local (coastal, littoral and estuarine) model applications.

The traditional, though in some way artificial, partition be-
tween spatial domain extent and model resolution, has
been also favored by the fact that operational ocean fore-
casting centres generate their specific ocean model products
for coastal and regional seas following a typical dynamical
downscaling approach, which transfers information at large
scales from the global solutions to the interior of the nest-
ed regional domains (Kourafalou et al., 2015). Spatial scales
are directly linked to temporal ones, and adequate tempo-
ral resolution is also needed to simulate ocean processes at
refined spatial resolution. Hydrodynamic model time steps
are always matched to spatial resolution by virtue of nu-
merical stability constraints, but consideration must be also
given to adding temporal resolution in external inputs, such
as specifying river inflow data at daily or shorter intervals,
and resolving in this way the diurnal cycle of solar heating. It
should be emphasized that temporal and spatial scales play
important roles in ocean model performance, and inappro-
priate decisions on the spatial-temporal scales to be solved
may result in modeling errors.

As pointed out by Holt et al. (2017), one of the greater chal-
lenges in Earth System Modeling science is to get an accu-
rate representation of coastal and shelf seas in global ocean
models. Furthermore, applying cutting-edge scientific prog-
ress in ocean model systems, which aim at solving the ocean
state in the climate system or at supporting monitoring and
forecasting systems, is another challenge of the operational
ocean services.

Next sections describe ocean forecasting at the global, re-
gional and coastal scales:

3.3.1. Global monitoring and
forecasting systems

Numerous ocean modeling groups and individual research-
ers operate near real-time systems for the analysis and
forecast of ocean mesoscale circulation in global and basin
scale domains. They are gathered under the umbrella of the
OceanPredict science network ( OceanPredict³) that evolved from the
GODAE group established in 1999 at the behest of the GOOS
sponsored OOPC panel. As a forum for knowledge exchange,
OceanPredict fosters communication on best practices and
new developments in global ocean modeling, engaging also
with regional domain activities and the generation of mod-

<figure><img src="image_42.png" /></figure>
el-based information products. A component of these ac-
tivities is the annual reporting on forecast systems run by
national centers and multi-national consortia, which main-
tain a very high level of operational stability and reliability
akin to national weather services. In many instances these
ocean systems increasingly operate in strict collaboration
with weather services, a trend that is strengthening with the
emergence of seasonal to sub-seasonal prediction efforts.
From OceanPredict Ⓓ<fnref n="4" /> annual reports and system descrip-
tions, it can be noted that horizontal resolutions of order
1/12°, or roughly 9 km at mid-latitudes, are widely considered
adequate for delivering forecasts useful to numerous stake-
holders and users, and to inform subsequent down-scaling
efforts. Running global simulations at much higher reso-
lution, such as the 1/48° (~2-3 km) MITgcm LLC4320 model
(Su et al., 2020), has proven feasible, and this resolution
improves the representation of processes such as the me-
soscale to submesoscale turbulent cascade and submeso-
scale modulated vertical mixing. However, for global forecast
systems, the substantial additional cost of advanced DA in-
creases the computational demand of the analysis/forecast
cycle by roughly an order of magnitude, making higher res-
olutions impractical at present. Moreover, there is evidence
that existing global observing networks are not able to con-
strain higher resolutions. Jacobs et al. (2019) suggest that the
horizontal scales of motion that are effectively constrained
by available sustained observations is of order 36 to 54 km or
larger, depending on the metric. When shorter length scales
that were notionally resolved by their model (~5 to 10 times
the grid resolution) but unconstrained by observation were
filtered out of the model prior to computing Lagrangian drift-
er trajectories, the ensemble trajectory forecast error actu-
ally decreased.

3.3.2. Regional monitoring and
forecasting systems

Many groups in the OceanPredict network also operate re-
gional domain models encompassing single ocean basins
or large marginal seas with enhanced resolutions of about
4 km or even finer, using output from global analysis/fore-
cast systems as open boundary conditions. There are many
reasons for this downscaling approach. The familiarity of
local experts with regional ocean dynamics allows them to
make choices in model configuration that yield more skill-
ful results. For data assimilative systems, there is also the
opportunity to incorporate local observations that were not
utilized by the parent model operators. Furthermore, region-
al operators are often better acquainted with, and can be
more responsive to, the information product requirements
of regional stakeholders.

The nominal resolutions of some typical regional systems
within OceanPredict Ⓓ<fnref n="5" /> are for the seas around Korea of 1
to 3 km and ports at 300 m, the MedFS at 1/24º (~3.5 km),
and the JMA at ~2.5 km. Numerous sub-domains in the Indian
Ocean run by the INCOIS operate at similar resolutions.

Some of these systems include advanced DA in the forecast
cycle initialization, such as the JMA regional model that uses
4-dimensional variational (4D-Var) assimilation, although this
is not the norm. Several Regional Associations of the US IOOS
operate down-scaling forecast systems using 4D-Var to incor-
porate local high-resolution data from autonomous vehicles
and surface current measuring HF-radar in domains of several
hundred kilometers in extent, but model resolution is in the
range 4 to 10 km. The WCOFS operated by the US NOAA CO-OPS
is an ambitious regional forecast system covering over 3000
km of the US west coast. Originally conceived as a 2-km model
(Kurapov et al., 2017), this proved impractical for real-time DA.
Operational WCOFS uses a 4 km grid, for which a complete cy-
cle of 4D-Var takes 5 wall clock hours each day on 480 cores
of the National Weather Service high performance computer.

However, computational cost remains a major constraint
on regional model resolution, and the question of whether
coastal ocean observing systems have sufficient resolution
to inform finer scales remains open. Mixed resolution sys-
tems are in development, wherein the forecast model is run
at a higher resolution than the DA analysis. In experimental
systems there is evidence (Levin et al., 2021) that submeso-
scale resolving nested models can extract added informa-
tion from closely spaced observing platforms that capture
the unbalanced ageostrophic submesoscale.

Sotillo et al. (2021) describe an operational system with
a model grid downscaling approach. It employs regional
downscaling to order 4 km with the purpose of delivering
improved resolution for continental shelf seas of the Ibe-
rian Peninsula, with subsequent downscaling to ~350 m on
selected coastal sectors and further to ~70 m in ports. This
hierarchical approach, using similar models at each level of
refinement raises a question: what are the differences be-
tween a coastal and a regional model?

The regional forecasting system examples mentioned above mostly use model codes that solve the hydrostatic primitive equations on a structured grid. While the transition to very high-resolution might admit submesoscale stratified dynamics, shallow coastal waters are often well mixed vertically and the processes relevant to ocean prediction for maritime operations have horizontal scales that are long relative to the depth, and consequently the hydrostatic approximation remains valid (Fringer et al., 2019).

<figure><img src="image_43.png" /></figure>

<footnote n="4">4. https://oceanpredict.org/science/operation-
al-ocean-forecasting-systems/system-descriptions</footnote>

<footnote n="5">5. https://oceanpredict.org/science/operational-ocean-forecasting-systems/system-reports</footnote>
The distinction we have made in structuring this section is that coastal models differ from regional models in that sub-mesoscale processes are dramatically constrained by bathymetry while coastline scales are smaller than the Rossby deformation scale. Such processes include lateral and vertical flow separation, secondary flows, headland eddies, wakes, and frontal convergences. Resolution of topographic features that impact such processes is of paramount importance.

### 3.3.3. Coastal monitoring and forecasting systems

As previously noted, Sotillo et al. (2021) used a set of structured grids in their limited area one-way downscaled nested coastal models for selected ports and coastal segments. One advantage of this strategy is that the computational burden of short time steps demanded by high resolution is limited to the finest grid nests and does not impact the efficiency of the coarse parent grid.

More complex nested systems employ full coupling of parent and child nests on each time step, including two-way communication of fine scale variability back to the parent, a feature supported in some models such as the Coastal and Regional Ocean Community model (CROCO; ⚫️⁶). A similar nesting framework has been used in the Regional Ocean Modeling System (ROMS; ⚫️⁷) model within the Coupled Ocean-Atmosphere-Wave-Sediment Transport system (COAWST; Warner et al. 2008) to perform numerous research studies of coastal and nearshore circulation and geomorphology, though implementation of this approach in operational systems is rare.

NOAA CO-OPS use the orthogonal curvilinear coordinate facility in ROMS to better represent details of irregular coastline shape and variable bathymetry in a number of estuaries of the U.S. coastal zone. For example, the Delaware Bay Operational Forecast System (DBOFS; ⚫️⁸) uses a curvilinear grid that adapts the model domain to the general shape of the estuary (Figure 3.2) and stretches the grid resolution from 4 km offshore to 40 m within the tidal river.

However, there are clear limits to the abilities and efficiencies of curvilinear structured grid models for coastal applications. By contrast, unstructured grid models (e.g. FVCOM, ADCIRC, SELFE, SUNTANS; see Fringer et al. (2019) for references on these models) have enormous flexibility to resolve complex bathymetric features. They efficiently resolve multiscale features by adapting grid orientation to follow the coastline or the isobaths, telescoping the resolution to match anticipated scales in the circulation.

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /><figcaption>**Figure 3.2.** NOAA CO-OPS Delaware Bay Operational Forecast System (DBOFS) curvilinear grid domain and bathymetry (top) and enlarged view of inset area (bottom) showing the stretch mesh ~40 m resolution in the vicinity of the estuarine salt wedge and tidal river.</figcaption></figure>

6. https://www.croco-ocean.org

7. https://www.myroms.org

8. https://tidesandcurrents.noaa.gov/ofs/dbofs/dbofs_info.html

<figure><img src="image_46.png" /></figure>
Conventional unstructured grid model configurations use the same time step throughout the domain, so regions of coarse resolution are often integrated with a time step vastly less than necessary for accuracy or stability, incurring in a loss of efficiency. But a well-crafted mesh will have a relatively small proportion of cells where the resolution is coarse. For example, the Great Barrier Reef model of Legrand et al. (2006) has 82% of the cells concentrated close to reefs and islands, whereas 25% of the area of the domain far from the coast is captured by less than 1% of cells.

As its name suggests, the SURF described by Trotta et al. (2021) demonstrates that both approaches to grid design can be implemented within a single system taking advantage of their respective strengths.

The ability of unstructured grids to resolve exceptional detail locally is illustrated by the application of the Stanford unstructured-grid, nonhydrostatic, parallel coastal ocean model (SUNTANS) to achieve ~1 m horizontal resolution at a convergence zone between tidal channels in the Snohomish River Estuary (Giddings et al., 2012). At this resolution, non-hydrostatic dynamics that are incorporated in the SUNTANS computational kernel can become important. However, in operational settings that encompass much larger domains, fully resolving such coastal submesoscale detail is not feasible, and some attempt at parameterization is necessary.

Approaches to parameterizing very high-resolution bathymetry in lower resolution models are discussed by Fringer et al. (2019), who draw particular attention to the sub-grid bathymetry method of Casulli (2009) for improved representation of wetting and drying processes for coastal sea level inundation. This approach, which preserves the cross-section area of cell faces on the basis of bathymetric data available at resolution finer than the model mesh, was used to great effect by MacWilliams et al. (2016) in simulations with the UnTRIM model (Casulli and Zanoli, 2005) of the San Francisco Estuary (Figure 3.3). Accuracy similar to the high-resolution (~10 m) version of the model was achieved with an order of magnitude fewer cells and a 40-fold speed-up in run time. For coastal inundation forecasting – an important application of operational coastal ocean modeling – is essential to follow these careful steps to represent coastal submesoscale bathymetric detail, as well as to achieve acceptable run-time for the timely delivery of forecast guidance.

The meaningful configuration of an operational system at such high resolution clearly requires the existence of comparable resolution bathymetric data. These are becoming more widely available with the increasing use of airborne LIDAR and concerted efforts to merge independently acquired data sets into unified gridded products with harmonized vertical datum. For example, coastal relief (both water and adjacent land) is digitized at 1/3 arc seconds (~10 m) for many sectors

<figure><img src="image_47.png" /><figcaption>**Figure 3.3.** Comparison between high-resolution (left) and coarse-resolution (right) from the Bay-Delta model grids by the UnTRIM model in the region of Mildred Island, San Francisco Estuary, U.S., showing the savings in resolution with little loss in accuracy by the application of sub-grid-scale bathymetry parameterization – from MacWilliams et al. (2016).</figcaption></figure>

of the US East coast that are subject to frequent storm surge inundation or at tsunami risk, and for most estuaries bathymetric data at 30 m resolution are available.

In contrast to the great challenge of adequately representing horizontal detail in coastal ocean models, vertical resolution is seldom a limitation in operational models. The widespread use in coastal models of terrain following coordinates retains vertical resolution in shallow water; in ROMS and CROCO this can be further stretched toward the surface or seafloor to give added resolution in frictional boundary layers.

Vertical turbulence closure schemes for operational coastal models are mature, including the parameterization of wave-current interaction processes that modify bed stress, wave radiation stress and Stokes drift, and models can exploit wave data or a wave model if they are available in conjunction with the circulation model. In this topic, there is active research and development on parameterizing the roles of sub-aquatic vegetation (Kalra et al. 2020) and semi-porous reefs on drag and circulation to adequately represent the drag in flooded areas during unusually severe inundation events.

Summarizing, the current best practices for multi-scale modeling from global to regional to coastal scales favor global and basin resolutions of order 1/12°, with downscaling to ~4 km in

<figure><img src="image_48.png" /></figure>
regional seas and sub-kilometer scale in coastal applications, estuaries and ports. This hierarchy of scales in typical applications was corroborated also in reviews such as Holt et al. (2017).

For coastal domains, unstructured grid models remain pop-
ular for the substantial flexibility they offer in representing
complex topographic regimes. At regional scales, the choice
for the appropriate model is wide and this is reflected in the
diversity of model codes used by operational agencies. It
should be kept in mind that resolution is only one constraint
on model fidelity. Forecast systems benefit from advanced
data assimilation in the analysis step that informs the initial
conditions of a forecast. While advanced data assimilation
at global and basin scales is mature and widely employed in
operational systems, these methods have yet to be applied
seriously in operational coastal and estuarine environments.

When this happens, aided by the emergence of comprehen-
sive high-resolution coastal observing networks, they place
an added burden on computational effort and may demand
reassessment of the resolution necessary to meet the infor-
mation requirements of stakeholders.

While it seems unlikely that very small scale nonhydrostatic
vertical processes will be resolved in operational systems in
the near future, there is progress on their parameterization
within conventional primitive equation models (e.g. Dong et
al., 2021). There is further ongoing research in both coastal
modeling techniques and parameterization of other process-
es (Fringer et al., 2019) for a comprehensive overview) and
many of these developments are expected to advance from
research to operations in due course.

3.4.

The temporal scales: different applications of numerical
modeling to solve ocean problems

The ocean displays variability of physical parameters across
a very wide range of spatial and temporal scales, from min-
utes to centuries and millennia and from centimeters to the
dimension of ocean basins (Benway et al., 2019). As shown
in Figure 3.4, this feature makes the ocean a greatly complex
system, characterized by interactions between a great deal
of processes at many different time/space scales (in which
small scales can affect large ones and vice versa).

Operational forecasting services, as defined in Section 3.1,
typically deal with problems with a forecast horizon from
hours to days, and time intervals at which the solutions are
presented to users can vary from hours to minutes. Neverthe-
less, ocean models can be used for other purposes at longer
time scales, such as seasonal prediction and climate model-
ing. Climate models are based on well-established physical
principles, and it has been shown that they can reproduce ob-
served features of recent climate and past climate changes.

There is considerable confidence that AOGCMs provide cred-
ible quantitative estimates of future climate change, partic-
ularly at large scales, although uncertainties still remain. As
stated in the Randall et al. (2007) contribution to the Physical
Science Basis Contribution of Working Group I to the Fourth
Assessment Report of the IPCC, there are different levels of
skill in simulating the various ECVs.

Long-term climate change projections reflect how human activ-
ities and/or natural effects can alter the climate over decades
and centuries. The principal driver of long-term warming is the
large cumulative emission of CO₂ over time from many an-
thropogenic sources. In this context, it is important defin-
ing scenarios, using specific time series of emissions, land
use, atmospheric concentrations or radiative forcing across
multiple models, which allows for coherent climate model
intercomparisons and synthesis. As stated by Collins et al.
(2013), for the above purpose is used information from a
range of different modeling tools, from simple energy bal-
ance models to the highly complex Earth System dynamical
climate models. The CMIP Phase 5 utilizes an unprecedented
level of information on base projections, including the more
complete representation of forcings, and has produced new
RCP scenarios (i.e. RCP2.6, RCP4.5, RCP6, and RCP8.5). Thanks
to the coordination of model experiments and outputs by
the CMIP5 group, the World Climate Research Program and
its Working Group on Climate Models have been able to step
up efforts to evaluate the ability of models to simulate past
and current climate and to compare future climate change
projections. This ‘multi-model’ approach is now a standard
technique used by the climate science community to gen-
erate and assess projections of a specific climate variable.

Substantial progress has been made in understanding the
climate scales, as well as in simulating important modes of

<figure><img src="image_49.png" /></figure>
<figure><img src="image_50.png" /></figure>

Source: modified from Dickey, 1991.

<caption>**Figure 3.4.** Temporal and spatial scales of selected ocean processes.</caption>

climate variability; as a consequence, the overall confidence in the capacity of models to represent important climate processes has increased. These improvements in AOGCMs are due in large part to the continuous development of the oceanic model component in recent years. There have been improvements in terms of resolution, computational methods, and parametrizations; furthermore, additional new processes have been progressively added to the ocean models used to simulate multi-year periods and climate projections, enhancing the complexity of the ocean climate model component.

As previously mentioned, ocean model resolution has increased (currently, the state-of-the-art is eddy-resolving models) and ocean climate models, especially regional models, are abandoning the 'rigid lid' treatment of the ocean surface that filters out some high frequency processes. New physical numerical parametrizations, including true freshwater fluxes, and/or improved river and estuary mixing schemes, better advection and mixing schemes are now widely used. All these improvements have led to the reduction of the uncertainty associated with the use of less sophisticated parametrizations. Finally, it should be mentioned that there

has been substantial progress in developing the cryospheric components of AOGCMs. Almost all state-of-the-art AOGCMs now include sea ice, with more elaborate sea ice dynamics, while many also include several sea ice thickness categories with relatively advanced thermodynamics and rheology.

Efforts to enhance the quality of climate projections are always related to the computational resources dedicated to the ocean modeling component, but currently there is no consensus on the optimal way to divide computer resources among the following components: i) finer numerical grids, which allow for better simulations; ii) greater numbers of ensemble members, which allow for better statistical estimates of uncertainty; and iii) inclusion of a more complete set of processes (e.g. carbon feedbacks). Finally, it has to be mentioned that there is also an important ongoing activity in terms of ocean climate regionalization, which has been developed in the framework of national and regional climate services initiatives with special emphasis on coastal climate impacts and applications.

<figure><img src="image_51.png" /></figure>
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MacWilliams, M., Bever, A.J. and Foresman, E. (2016). 3-D simulations of the San Francisco Estuary with subgrid bathymetry to explore long-term trends in salinity distribution and fish abundance. *San Francisco Estuary and Watershed Science*, 14(2), https://doi.org/10.15447/sfews.2016v14iss2art3

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<figure><img src="image_54.png" /></figure>
4.
Architecture of ocean monitoring
and forecasting systems

<figure><img src="image_55.png" /></figure>

CHAPTER COORDINATOR

Avichal Mehra

CHAPTER AUTHORS (in alphabetical order)

Roland Aznar, Stefania Ciliberti, Laurence Crosnier, Marie Drevillon, Yann Drillet, Begoña

Pérez Gómez, Antonio Reppucci, Joseph Sudheer, Marcos Garcia Sotillo, Marina Tonani, P. N.

Vinaychandranand, and Aihong Zhong.

<figure><img src="image_56.png" /></figure>
# 4. Architecture of ocean monitoring and forecasting systems

<figure><img src="image_57.png" /></figure>

## 4.1. Modelling systems architecture

### 4.1.1. Step 1 processes

4.1.1.1. Data access and pre-processing

4.1.1.2. Data assimilation: analysed fields

### 4.1.2. Step 2 processes

4.1.2.1. Forecast

### 4.1.3. Step 3 processes

4.1.3.1. Post-processing

4.1.3.2. Validation

4.1.3.3. Dissemination

4.1.3.4. Monitoring

## 4.2. Inputs required

### 4.2.1. Obtaining and preparing ocean data

4.2.1.1. Data retrieval and characterization

4.2.1.2. Quality Control

4.2.1.3. Data Formats

4.2.1.4. Data Delivery

### 4.2.2. Description of existing in-situ observational oceanographic data

4.2.2.1. Buoys

4.2.2.2. Tide gauges

4.2.2.3. Argo

4.2.2.4. Ship-of-opportunity program

4.2.2.5. Gliders

4.2.2.6. HF radars

4.2.2.7. Marine Mammals CTDs

4.2.2.8. Autonomous underwater vehicles

4.2.2.9. List of most relevant international in-situ data providers

### 4.2.3. Description of satellite observational oceanographic data

4.2.3.1. Satellite sea surface temperature

4.2.3.2. Satellite Altimeter

4.2.3.3. Satellite Sea Surface Salinity

<figure><img src="image_58.png" /></figure>
4.2.3.4. Satellite sea ice

4.2.3.5. Ocean Colour

4.2.3.6. Significant Wave Height

4.2.3.7. Providers of satellite data

4.2.4. Bathymetry

4.2.5. Atmospheric forcing

4.2.6. Land forcing

4.2.7. OOFS fields as input for downscaling

4.2.8. Climatology from observations

## 4.3. Data Assimilation

## 4.4. Numerical Ocean models

4.4.1. Definition and types of models

4.4.2. Coupled models

## 4.5. Validation and Verification

4.5.1. Basis statistical tools for time series validation

4.5.2. Ocean forecasting standard metrics for validation and intercomparison

4.5.3. Qualification, validation and verification processes in support of operational
ocean models' production

## 4.6. Output preparation

4.6.1. Introduction

4.6.2. Products and datasets

4.6.3. Variables

4.6.4. Spatial resolution

4.6.5. Time resolution

4.6.6. Data format

4.6.7. Display and analysis tools

4.6.8. Output dissemination

## 4.7. User management and outreach

## 4.8. References

<figure><img src="image_59.png" /></figure>
4.1.
Modelling systems architecture

An OOFs, with a global to regional scale, is based on numer-
ical modelling of the ocean dynamics, biogeochemistry, and
wave and data assimilation schemes for blending observa-
tions into the model and for providing the most accurate ini-
tial condition for the forecast (Tonani et al. 2015). An OOFs
at coastal scale may usually use information from global/
regional scales in terms of initial and boundary conditions to
initialise and force its ocean model core in a very limited area
in order to provide very accurate spatial-temporal solutions
and may not necessarily use data assimilation methods.

In general, to produce a forecast we need to:

1. know what the ocean is doing now (initial condition);

2. calculate how the ocean will change in future (forecast);

3. use oceanographic expertise to validate and refine the output (products).

These three steps, represented in Figure 4.1, are based on a
few basic components: observations, numerical model, and
oceanographic expertise. Most of the systems rely on data
assimilation techniques (see Section 4.4 for a general intro-
duction and Section 5.5 for more details about numerical
schemes) for blending observation and models; therefore,
data assimilation can be considered as one of the essential

components of the system. In the case of coastal forecasting
systems, downscaling from global/regional scale is the pre-
ferred approach as described in Section 5.4.4.

Step 1 is the production of the most accurate initial condi-
tion about the variables the forecasting system is aiming to
predict. This means that we need the best knowledge of the
present status of each variable at every model grid point. This
information is difficult to retrieve from observations because
their spatial/temporal coverage is usually very sparse. Model
simulations instead provide a uniform coverage in space and
time and, thanks to data assimilation techniques observa-
tions, they can be blended into the model simulation, im-
proving their accuracy. For data assimilation, it is common to
use observations from multiple sources, maximising the data
coverage and the type of variables measured by in situ and
satellite instruments. The initial condition for the forecast is
usually the result of a complex set of multiple simulations
with data assimilation covering past hours or days. For global
and regional oceanographic systems it is common to have
a data assimilation cycle of the order of a few days. These
simulations of the past provide not only the best knowledge
for initialising the forecast of the present but also valuable
information on the near present that can be included in the
final product delivered to the users.

<figure><img src="image_60.png" /><figcaption>**Figure 4.1.** Scheme of steps and main components of a forecasting system and of its architecture.</figcaption></figure>
The model usually needs some external forcing as input. The type of information needed at its boundaries (e.g. ocean/ atmosphere, lateral boundaries, along the coast, etc.) can vary from model to model. An ocean dynamical model usually needs an atmospheric forcing from a real time weather prediction system to resolve the processes at the ocean/atmosphere interface. A regional/coastal model requires river runoff data at the interface with the coast and input values for its variables at the lateral boundaries. In case of coupled models (see Chapter 5 and Chapter 10, for example), external forcing fields might not be needed.

Step 2 is the projection in the future, the production of the forecast that is done by running the numerical model for hours, days or months in the future. The forecast lead time can vary from hours to days. Many systems have a forecast lead time of 3-15 days. The same forcing fields described in Step 1 are needed also for the forecast. The forcing fields could be from another forecast like the atmospheric forcing, that usually is from a weather prediction system, or they can be provided by climatological values or persisting the last available value.

Once the model has produced the forecast, it is validated and its output post processed to a standard format for the delivery to the users (Step 3 in Figure 4.1). The validation of the forecast cannot be done via direct inter-comparison with observations but is based on the validation of its initial condition and on studies covering an extended period in the past of the model skills.

As explained before, observations are a key component but have to be made available in real time and in a standard format. Observations in real time are usually ready to be used within a few hours from their acquisition but sometimes they can have delays of more than 24 hours. Timing of data availability will influence the design of the production cycle that has to compromise between using the maximum number of the observations and reducing the delay in the forecast release. The choice to be made has also to consider the need to release a new forecast as soon as possible even if this could imply a degradation of its accuracy.

The timeliness of the forcing fields is another limiting factor in the design of the production chain. We can take as an example a wave forecasting system in which the accuracy of the predicted fields is strongly correlated with the accuracy of the winds. We have to wait until the latest and more accurate wind forecast is made available before starting our production. Different solutions can be implemented depending on the characteristics of each system. The computational time needed for running each of the three steps described is a very important aspect as, depending on the cost for running a specific system, it could be a limiting factor.

Timeliness is of paramount importance for the users and the production time should be reasonably short to avoid delivering forecasts referring to the past. A rule of thumb is that the production time needs to be consistently less than the production frequency. It means that for a daily cycle (production of a forecast once a day) the production time should be of the order of a few hours.

Even if the information provided in this section is focused on a forecasting system, with few modifications it can be also applied to a multi-year production system to produce a reanalysis. The main difference is that in this case you are not projecting in the future but in the past. This implies that you can blend observations and model simulations at each time step. The model is continuously corrected by the observations, increasing the accuracy of the simulations. The atmospheric forcing usually is also more accurate because it is an analysis and not a forecast, and hence the observations have been subject to a more restrictive data quality control compared to the real time ones.

The multi-year production is composed only of Step 1 and Step 3. In this case, in Step 1 the model and data assimilation cover a few hours/days spans over multiple decades of years. As the multi-year products are not limited by the timeliness, usually their major constraints are the computational time that can be extremely expensive as well as the availability of homogenous sources of forcing. These differences with respect to other forecast products have to be taken into account in the design of the production cycle.

In the next subsections the architecture details at the basis of an OOFS will be introduced.

### 4.1.1. Step 1 processes

#### 4.1.1.1. Data access and pre-processing

The data access and pre-processing component should make available all the needed dataset that will be used to perform the analysis, and then the forecast (Step 2). Automatic acquisition of the data is mandatory for an operational system. It could be quite demanding depending on the dataset, the centres (or data providers) involved in data production and treatment, and the available network to connect the centres. For most of the dataset used in OOFS, at least a daily update is needed.

For atmospheric forcing the volume of the dataset can be big, and an efficient connection to Operational Meteorological Centres in charge of operational production of atmospheric analysis and forecast is critical. For example, the volume of hourly surface forcing fields from the ECMWF at global scale is 34 GB per day. Then, data pre-processing is necessary to interpolate the atmospheric fields to the ocean grid, if there

<figure><img src="image_61.png" /></figure>
is inhomogeneity between frequency of available forcings during the length of the specific run, atmospheric datasets must also be interpolated temporally. When a regional ocean model is employed instead of a global model, the retreatment of the atmospheric dataset may substantially reduce the volume of the atmospheric dataset and reduce the overall storage cost.

In-situ ocean observations can be downloaded in real time using WMO GTS or from dedicated interface such as the service developed in the Copernicus Marine Service (Le Traon et al., 2019), in which in situ observations are made available, documented, quality controlled, and homogenised, all very important tasks to be performed before assimilating such dataset in an OOFS. Satellite observations need to be pre-processed by a dedicated centre before their assimilation in an ocean operational system. Satellite observations are processed at various levels ranging from Level 0 to Level 4 which need to be made available depending on the data type. For example, Copernicus Marine Service also provides a unique access point to download all the available satellite observations in real time.

### 4.1.1.2. Data assimilation: analysed fields

Ocean analysis is based on a model, observations, and data assimilation scheme to provide the initial state of the forecast on the basis of a minimum error principle, i.e. the data assimilation modelling system (Figure 4.1). This component is central processing unit (CPU) consuming and should be performed on a supercomputer. High performance computing power is one of the most important constraints to define the resolution of the analysis system, along with the number of observations that will be assimilated in the system and the frequency and length of the analysis cycle. In an operational framework, the analysis cycle should be performed in a range of a few minutes to a few hours (maximum), choosing the best compromise between performances, quality of the analysis, and robustness of the operational system. This component will provide the initial state for the ocean forecast. The resulting time series of analysed ocean state is defined as the best analysis time series.

To perform an ocean analysis, we need the initial state of the model, based on the prior state of the model at the end of the previous analysis cycle, in situ and satellite observations, and atmospheric forcing analysis fields, collected and formatted in the previous acquisition and pre-processing phase (including all the static files that are necessary for the data assimilation modelling system). Outputs of this component are 3D fields to update the best analysis time series and restart files to initialise the next ocean forecast. Other diagnostics, metrics or post-processing may be computed online directly during the analysis cycle to optimise the system, and used as additional products for dissemination and archiving.

Such products are also used during the validation phase (e.g. the mixed layer depth, the collocation between model output and observations, transports, etc.).

Note that in some coastal forecasting systems there is no direct data assimilation. If the model domain is small, in some occasions there is simply no available data to be assimilated. In these cases, the system relies totally on the boundary conditions and initial 3D fields derived from a larger scale model (see Section 5.4.4 for downscaling examples).

## 4.1.2. Step 2 processes

### 4.1.2.1. Forecast

The ocean forecast at some range is based on the numerical model initialised by the ocean analysis and forced by the atmospheric forecast fields as provided by the operational atmospheric centre. In most cases, the same model is used for both the forecast component and the analysis component, even if differences in terms of resolution and physical parameterizations could be envisaged especially in the framework of an ensemble forecast. The same constraints mentioned above about high performance computing apply in order to perform forecasts that are usually updated at least every day. Forecast range will also depend on the computing resources and on the main processes that have to be forecasted with a reasonable skill (to be defined by the developer of the forecasting system). The forecasting cycle should be performed in a range of a few minutes to a few hours. Inputs of the forecasting cycle are the initial state produced by the data assimilation modelling system (e.g. ocean analysis), all the static files needed to integrate the model, and the atmospheric forcing for the full forecast length. The forecast output is updated every day and consists of 3D and 2D ocean fields; it may include diagnostics, metrics and other post-processed dataset that can be useful to assess the quality of the product, to highlight specific features of the forecasted ocean properties and for the final delivery to users.

## 4.1.3. Step 3 processes

### 4.1.3.1. Post-processing

The post processing phase is devoted to building all the products that will be delivered to the users. It consists of files or datasets that are provided according to a) standard file format (e.g. according to CF Conventions, 📄<fnref n="1" />); b) on a specific grid; and c) with homogeneous variables and metadata. Such products may be then used to compute new products as ocean monitoring indicator (OMI), ensemble mean and standard deviation in the framework of ensemble forecast.

<figure><img src="image_62.png" /></figure>

<footnote n="1">1. https://cfconventions.org/</footnote>
This post processing should be performed on a supercomputer in which all datasets provided by the analysis and forecast components are stored in order to save resources in the computing centre. Computing cost of this stage could be really high (for example, due to the interpolation procedure in the case that the products are delivered on a specific grid) and would also include large data transfer and input/output access. The inputs of the post-processing component are represented by all datasets produced during the analysis and forecast cycles, while the outputs are all the products that will be delivered for internal and external users.

#### 4.1.3.2. Validation

The objective of the validation component is to provide information on the quality of the operational system. The quality of the analysis is compared to already known or expected results (based on literature or climatological datasets) or to available observations. Quality of the forecast is performed by computing forecast skill in comparison to the analysis with the observation in delayed mode. The final step is to provide all this information to forecasters and users. Input of this component are model products, diagnostics and metrics computed during previous steps and the output could be numerical fields, time series and/or interactive maps that allows, through web interfaces or other kinds of applications, direct querying, comparison of different periods, and validation of the production.

#### 4.1.3.3. Dissemination

The goal of the dissemination phase is to make all the products available to users on a dedicated infrastructure. This phase may be complex and the associated cost is very dependent on objectives and user needs. If the dissemination of the model is only internal, outputs could be made available through an intranet, using in-place storage capacities. Other approaches are mandatory for a more complex system providing a very large dataset and long-time series and designed to be accessed by several thousands of users, including a catalogue of products continuously maintained and updated, dedicated services for viewing, extracting and downloading the data. Cloud storage facilities are now the best infrastructure to disseminate operational ocean products.

#### 4.1.3.4. Monitoring

The monitoring component is an important part of an operational system as it allows operators and forecasters to monitor the performances along all the production phases, from data access to dissemination. KPIs should be monitored during this phase, including availability of inputs and outputs during each phase, timeliness, time of delivery and delay of each component, anomaly and/or errors identified during each phase. Monitoring phase should be used to provide information to the users and to decide on a go/no-go to disseminate the products externally. Monitoring phase should be presented on a dedicated dashboard.

## 4.2.
Inputs required

To run an OOFS as part of Step 1, the following sources of information are needed:

* Observations of EOVs are extremely important for an OOFS as they are used for assimilation and validation purposes. The main sources of observations are:

* In-situ observations:

* **Buoys.** Typically used to measure directional waves, atmospheric parameters (wind, atmospheric pressure and air temperature), EOVs (currents, temperature and salinity) and, less frequently, biogeochemical parameters. Some stations measure only on the surface, while others extend their observations to the whole water column. These variables are used for all kinds of OOFS: Wave in-

formation is critical for validation and is occasionally used in assimilation; oceanographic data are widely used in circulation modelling and the scarse biogeochemical stations are critical to complement the existing climatological data;

* **Tide gauges.** Measuring sea level, tide gauges are extremely useful for the validation of storm surge and circulation models, sometimes also used in data assimilation. In recent times, with the increased frequency sampling of modern tide gauges their use to validate wave models in coastal regions has extended;

* **Argo drifters.** Typically measuring profiles of salinity and temperature. More recently, bio-geochemical parameters are also being incorporated.

<figure><img src="image_63.png" /></figure>
This is an essential source of information for large
scale circulation modelling;

* **Ship-of-opportunity.** Usually measuring SST and SSS via thermosalinograph or releasing Expendable Bathythermograph to measure temperature throughout the water column. These data are usually employed for circulation modelling;

* **Gliders.** Gliders can provide a 3D field of ocean structures that can be highly valuable for validation of circulation modelling and assimilation in regional and coastal scales. Gliders can also provide valuable biogeochemical information;

* **HF radars.** The surface current fields are used for validation and data assimilation in circulation models. Additionally, the wave measurements can be used for validation in wave forecasting systems;

* **Marine Mammals CTDs.** As in the case of the gliders, this is an increasingly important source of information that allows us to gather detailed information on small-scale ocean and coastal features.

* Satellite observations provide information on the following variables:

* **Sea level anomaly.** These data are a critical variable for data assimilation in large scale circulation models;

* **Sea surface temperature.** As the previous, usually it is employed in data assimilation as well as in validation of ocean circulation forecast systems;

* **Sea ice concentration.** Used for both validation and data assimilation in ice models, coupled to circulation models;

* **Waves.** This variable is being used in large scale wave forecast systems for data assimilation and, on some occasions, for validation;

* **Ocean colour.** Mainly employed for assimilation and validation in biogeochemical models. Can also be used as a secondary source for validation in circulations, since sometimes coastal structures are evident.

* Bathymetric datasets. Bathymetry is at the base of every OOFS and, therefore, it is indispensable for all systems;

* Surface forcing. Provided by operational NWP systems. These data are used for describing air-sea-sea

ice interactions. Momentum, heat and freshwater fluxes are of paramount importance for all the processes at sea. Therefore this forcing is needed in almost any OOFS, with only a few exceptions (for example, some very high resolution wave propagation systems can operate without it, because the influence of forcing is already considered on other larger scales);

* Land forcing fields (i.e. discharge of water and nutrients from rivers). Mainly used in circulation and biogeochemical modelling. This source of data is very relevant to provide accurate solutions at the coastline. Unfortunately, on some occasions real time data are not available and the modellers must rely on climatologies;

* Ocean fields. They are provided by OOFS at larger scale to work as initial and boundary conditions (for example 3D temperature fields for downscaling applications in circulation modelling). When nesting, it is indispensable to have these fields. It is a frequent technique in all kinds of regional scale and coastal OOFS;

* Climatologies. Sometimes climatologies are employed for validation or initialization when no other data are available. These data sources are also employed in validation processes, to check that the models do not depart too much from real values in regions where measurements are not frequent.

The following sections contain first an introduction on how to deal with ocean data from the perspective of the data provider, and then a description of the above mentioned data sources, including a list of international providers.

### 4.2.1. Obtaining and preparing ocean data

The quality of OOFS products is highly dependent on the availability of in situ and satellite observations; these are used, through data-assimilation, to constrain the analysis and the forecasting systems, and validate their outputs. However, prior to use these observations, they need to be properly retrieved, efficiently organised, and carefully quality controlled (Le Traon et al., 2009). In the architecture of an OOFS, this is accomplished by the so-called DMS, the data management component. The ultimate goal of this system is to ease the use of oceanographic observations, providing consistent and harmonised products ready to be used for data assimilation and validation.

Figure 4.2 shows how data flow should be organised in a DMS. To get the most out of information, a DMS is responsible for gathering and organising the ocean observations (satellite and in-situ) in high-quality products and then to disseminate them in a timely fashion that meets the requirements of modelling and data assimilation centres. Once acquired,

<figure><img src="image_64.png" /></figure>
<figure><img src="image_65.png" /><figcaption>**Figure 4.2.** Typical DMS data flow from upstream international networks for OOFS.</figcaption></figure>

observation must be supplemented by uncertainty estimates and quality flags (part of the quality control process), which are key for validation and data assimilation. Then, they are prepared according to the specific file formats and distributed to users.

### 4.2.1.1. Data retrieval and characterization

First task of a DMS is to gather observations available from selected data providers (e.g. space agencies, international in-situ data networks, etc.). The choice of observations to be retrieved, processed and delivered depends on a previous analysis of the needs expressed by the prediction systems. In general, a tight coordination, upstream with data providers and downstream with prediction systems, is necessary to keep needs updated and ensure that the required observations are provided timely.

Ocean observations are made using several sensors, including in situ and remotely sensed ones, covering a broad range of spatial and temporal scales. Ocean observations made by remote sensing sensors usually include data for monitoring sea level, SST, salinity, surface wind and currents, sea ice, and ocean colour; these observations are acquired on a global basis and distributed at several different levels of processing, ranging from raw data to detected geophysical variables. Space Agencies (e.g. ESA, NASA, EUMETSAT, JAXA) are responsible for the provision of such observations.

In-situ observations are of paramount importance for OOFS because they provide information about the ocean interior that cannot be observed from space. In-situ observations can locally sample high-frequency and high-resolution ocean

processes, in particular in the coastal zone, that are essential for model and satellite validation activities. In-situ observations are acquired through various network programs at both global and regional scale.

Data from a global prediction system, to be used to define boundary conditions of a nested regional one, or terrain/ atmospheric forcing in certain scenarios will be part of the data to be inputted in the prediction system.

Knowledge of the processes that have been undertaken to produce a given observation and its characteristics is of high importance, as it allows a user to decide upon the product's fitness for a particular application. To this end, it is important to ensure that metadata associated with each of the retrieved dataset contain the appropriate information (e.g. instrument/platform characteristics, tests performed and failed, origins of the data stream, data processing history, and information about the datasets).

### 4.2.1.2. Quality Control

In general, a Prediction System needs two types of input data. Initially NRT data are needed for hourly to weekly forecasting activities; at a later stage and for applications in which long-term stability is needed (e.g. reanalysis, climate monitoring, and seasonal forecasting), DM data comes into play. Due to their different utilisation, quality control procedures for the two types of data are applied in different ways and with different methodologies.

NRT input data, delivered within a few hours to maximum one week from acquisition, are usually automatically quality

<figure><img src="image_66.png" /></figure>
<table><caption>**Table 4.1.** Copernicus Marine quality control flags as applied to Global Ocean In-Situ Near-Real-Time Observations product (INSITU_GLO_NRT_ OBSERVATIONS_013_030, 📈<fnref n="2" />).</caption><thead><tr><td>Code</td><td>Meaning</td><td>Comment</td></tr></thead><tbody><tr><td>0</td><td>No QC was performed</td><td>-</td></tr><tr><td>1</td><td>Good data</td><td>All real time QC tests passed.</td></tr><tr><td>2</td><td>Probably good data</td><td>These data should be used with caution.</td></tr><tr><td>3</td><td>Bad data that are potentially correctable</td><td>These data are not to be used without scientific correction.</td></tr><tr><td>4</td><td>Bad data</td><td>Data have failed one or more of the tests.</td></tr><tr><td>5</td><td>Value changed</td><td>Data may be recovered after transmission error.</td></tr><tr><td>6</td><td>Value below detection/quantification</td><td>The level of the measured phenomenon was too small to be detected/ quantified by the technique employed to measure it. The accompanying value is the detection/quantification limit for the technique or zero if that value is unknown.</td></tr><tr><td>7</td><td>Nominal value</td><td>-</td></tr><tr><td>8</td><td>Interpolated value</td><td>Missing data may be interpolated from neighbouring data in space or time.</td></tr><tr><td>9</td><td>Missing value</td><td>-</td></tr></tbody></table>

controlled using a priori agreed upon procedures. For in-situ observations, quality control tests aim mainly at detecting outliers; these procedures check for inconsistencies in the measurements often using local statistics built from a long time series of similar data. Quality control of remotely sensed observations is performed by comparisons with in-situ observations when available, or by comparison to long-time series (i.e. climatologies) derived from the same product. These procedures aim at defining the accuracy of the product and detecting anomalous observations. As a result, for both in-situ and remotely sensed NRT products, quality flags are positioned to inform the users about the level of confidence and, where possible, the level of accuracy attached to the observations.

In-situ DM data are usually subject to an off-line quality control using statistical tests to check for spatial consistency and to a much more refined climatology test, usually with strong involvement of scientific experts in the quality-control process. Satellite observations delivered in DM usually

benefit from improved ancillary data (e.g. more precise satellite ephemerides, meteorological reanalysis, etc.) used in the retrieval process, resulting in a more accurate product.

Besides the activities aimed at establishing the quality of the required observations, a DMS shall also monitor the performance of the different providers in terms of availability, possible degradation of their sampling, and timeliness. This additional information also needs to be regularly provided to prediction systems making use of these observations.

A DMS should also set up a procedure to gather, in form of reports, regular information on the data that have not been used by the prediction systems, because they were deemed to be of inadequate quality; this procedure, often called “Blacklisting”, has significant value for improving automated procedures for data quality control.

Table 4.1 shows the standard quality control (QC) indexes assigned to Copernicus Marine Service in-situ and satellite data.

<figure><img src="image_67.png" /></figure>

<footnote n="2">2. https://doi.org/10.48670/moi-00036</footnote>
#### 4.2.1.3. Data Formats

Observations usually arrive at a DMS in a variety of formats, depending on the platform being used to acquire and broadcast them or on the software used to retrieve the variables of interest. For ease of use, a DMS will format all the incoming observations in data structures which satisfy the OOFS requirements. Data formats are usually defined during the development of the OOFS infrastructure in coordination with the prediction systems and detailed in dedicated documents. Besides a detailed description of the format in which the data or products will be stored, key subjects to be addressed in such documentation include:

* standards that will be used to build the data structures hosting the incoming observations (e.g. NetCDF format);

* semantics, provided by a recognized common convention (e.g., CF), which are then used to write metadata; and

* a description of the transformation algorithms for all data handling (e.g. transformation algorithms to/from standards).

To enhance interoperability and sharing of data, non-proprietary solutions commonly used by the community are favoured during the selection of data format.

#### 4.2.1.4. Data Delivery

The ultimate task of a DMS is to deliver datasets required for assimilation and validation activities to prediction systems, including uncertainty estimates that are critical for the effective use of the data. For the best possible exploitation of this data, an easy-to-access and robust service to visualise and access present and past available observations and associated metadata must be deployed. Metadata include latency information on data availability as a key parameter in the data flow. It is important that new observations are made accessible to the prediction systems with the shortest possible delay.

Access to data can be achieved in different ways:

* "Pull services" enable users to request data according to their needs; this type of service should integrate tools that allow constraining the area of interest and time covered by the information;

* "Push Services" are often based on subscription, which literally push the data to users following prescribed specific requirements.

Beyond visual navigation of data, a dissemination service should also include utility tools allowing transformation (e.g. format conversion and coordinate transformation), aggregation, and integration of a given variable regardless of source.

Another aspect to be considered as key for a successful dissemination service is the ability to perform appropriate extractions according to different data geometries (e.g. gridded datasets, unstructured gridded data, vertical profiles etc.).

### 4.2.2. Description of existing in-situ observational oceanographic data

In the next sections, it will be introduced the main observational oceanographic data from in-situ platforms used by OOFS. Details about their usage in numerical modelling and validation, as well as providers, are described in Chapters 5 to 9.

#### 4.2.2.1. Buoys

Operational drifting buoys are a primary source of data on ocean surface conditions. They are deployed and maintained by autonomous groups, subject to different intergovernmental agreements, under the coordination of the Data Buoy Co-operation Panel (DBCP, 🌐³). The Global Drifter Program (GDP) works in collaboration with national meteorological/oceanic agencies to routinely deploy large quantities of drifting buoys in support of their research and operational programs. Maintaining drifting buoy density distribution is a major challenge, due to the difficulty of high latitude deployments and because Lagrangian drifting buoys follow ocean currents and tend to cluster together near convergence zones.

Moored buoys are anchored at fixed locations, reporting temperature and salinity profiles, and are concentrated mostly in the tropical oceans and the coastal regions of Brazil, Europe, India, and the United States (⁴). The different programs/agencies responsible for handling the tropical mooring networks are:

* the Tropical Atmosphere-Ocean/Triangle Trans-Ocean Buoy Network in the equatorial Pacific (TAO/TRITON) (McPhaden et al., 1998);

* the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) (Bourlès et al., 2008);

* the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean (McPhaden et al., 2009).

3. https://www.ocean-ops.org/board

4. https://www.ocean-ops.org/dbcp/platforms/types.html

<figure><img src="image_68.png" /></figure>
The TAO/TRITON, PIRATA and RAMA moored arrays are part of the DBCP's moored buoy network through the Tropical Moored Buoy Implementation Panel (TIP).

Data from the DBCP GBN is transmitted through the GTS of the WMO and archived by the operational agencies. At present, the GBN has over 1,380 drifting buoys and 260 coastal/national moored buoys and 70 tropical arrays. While COVID-19 restrictions imposed stress on deployment opportunities, the drifting and moored buoy networks successfully maintained a healthy and resilient status in data quantity, quality, coverage and timeliness, due to the prolonged lifetime and improved performance of buoys (⁵).

### 4.2.2.2. Tide gauges

Tide gauges are instruments on fixed platforms, located usually along the coastline, that measure water level with respect to a local height reference. Their primary objective is to support coastal zone monitoring and management, tide prediction, datum definition, harbour operations and navigation; additionally, they are used in sea level hazard warning systems, for climate monitoring, model validation and assimilation, and to detect errors and drifts in satellite altimetry. Tide gauge data complement the sea surface height data provided by the spatial altimeters, by providing higher temporal sampling (up to 1 min or less, allowing detection of higher resolution sea level phenomena) from in-situ data at the coast, where the quality of altimetry is lower.

The Global Sea Level Observing System (GLOSS; <fnref n="6" />) is the main international program responsible for collection, quality-control and archiving of tide gauge observations. The following data centres contribute to GLOSS data services:

* PSMSL (7), responsible for the global database of monthly and annual mean sea levels for long-term sea level change studies from tide gauges (8);

* UHSLC (9), in which high-frequency tide gauge data (hourly and daily) can be found. Two datasets are provided, with different levels of quality control: research quality (updated annually) and Fast-Delivery (updated every 1-2 months);

* IOC Sea Level Station Monitoring Facility (IOC/SLSMF: 10), maintained by Flanders Marine Institute (Belgium), provides access to real-time raw tide gauge data, with shorter time sampling (< 1min) for tsunami monitoring;

* SONEL (11) is the GLOSS data centre for GNSS time series at tide gauge locations, if available. This information is the source of vertical land movement at the site and provides an ellipsoidal height reference of the tide gauge.

Figure 4.3 shows the global distribution of tide gauges together with the total number of installed stations from 1800 to 2000s (Hamlington et al. 2016), collected by the PSMSL. It shows the sparse distribution of tide gauges stations in some areas, such as Africa and South America.

The EuroGOOS launched an initiative through its dedicated Tide Gauge Task Team (TGTT) working group (12) to capitalise the expertise, usage and further improvement of the tide gauges network in the continent. This working group has launched several actions to enhance the connection between GLOSS and European data portals such as EMODnet and Copernicus Marine Service. These data portals integrate tide gauge data with other in situ, satellite and model data, and provide a one-point access for most of the tide gauges data for operational and scientific activities.

### 4.2.2.3. Argo

Argo is a global array of approximately 4,000 free-drifting profiling floats, designed to measure the temperature and salinity of the upper 2,000m of the ocean. The array covers the global ocean reasonably well and is one of the main in-situ observation data sources for ocean data assimilation and validation.

Each standard float has a resting depth of 1000m for 9 days. Every 10 days it is programmed to descend to 2000 m and then ascend to the surface measuring temperature and salinity in the ocean column. Data is transmitted via satellite and distributed on the GTS in BUFR code. Similar real-time quality-controlled Argo profiles can be obtained from two Global Data Assembly Centres (GDACs) - based one in Monterey, USA, and the other in Brest, France - that were set up as part of the international GODAE. For their behind real-time analyses, some operational centres use real-time Argo floats from both the GTS and the two GDACs.

<figure><img src="image_69.png" /></figure>

<footnote n="5">5. https://public.wmo.int/en/media/news/ocean-observing-system-report-card-2020</footnote>

<footnote n="6">6. http://www.gloss-sealevel.org</footnote>

<footnote n="7">7. https://www.psmsl.org/</footnote>

<footnote n="8">8. https://www.psmsl.org/</footnote>

<footnote n="9">9. http://uhslc.soest.hawaii.edu</footnote>

<footnote n="10">10. http://www.ioc-sealevelmonitoring.org</footnote>

<footnote n="11">11. http://www.sonel.org</footnote>

<footnote n="12">12. https://eurogoos.eu/tide-gauge-task-team/</footnote>
<figure><img src="image_70.png" /><figcaption>**Figure 4.3.** Top: global spatial distribution of the 1420 tide gauges in the PSMSL RLR dataset. Bottom: number of available tide gauges in the PSMSL RLR dataset through time (blue). Available gauges for the Northern Hemisphere (red) and Southern Hemisphere (black) are also shown for comparison (source: 🌐<fnref n="13" />).</figcaption></figure>

By 2020, Argo is collecting 12,000 data profiles each month (400 a day). The most updated picture of available operational Argo at global scale is shown in Figure 4.4. Further details are available at 🌐<fnref n="14" />. There was a slight 10% decrease in daily data flow in early January 2021, but overall spatial-temporal coverage has progressed since 2020 despite the challenges of the worldwide pandemic.

Satellite-tracked surface drifting buoys are extremely cheap and useful to measure mixed layer currents, sea surface temperature, atmospheric pressure, winds, and salinity. They are part of the GDP and are able to reach a maximum 15 m depth. An updated map of operational surface drifters is shown in Figure 4.5. Further information is available at 🌐<fnref n="15" />.

<figure><img src="image_71.png" /></figure>

<footnote n="13">13. https://climatedataguide.ucar.edu/climate-data/tide-gauge-sea-level-data</footnote>

<footnote n="14">14. https://argo.ucsd.edu</footnote>

<footnote n="15">15. https://www.aoml.noaa.gov/phod/gdp/index.php</footnote>
<figure><img src="image_72.png" /><figcaption>**Figure 4.4.** Global distribution of Argo network in January 2021 (source: 👤¹⁶).</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>**Figure 4.5.** Global distribution of drifting buoys and moored buoys in January 2021, concentrated mostly in tropical oceans and coastal regions of Brazil, Europe, India, and the United States (source: 👤¹⁷).</figcaption></figure>

<figure><img src="image_74.png" /></figure>
4.2.2.4. Ship-of-opportunity program

<figure><img src="image_75.png" /><figcaption>**Figure 4.6.** The network status of global XBT lines provided from Ocean-OPS in December 2020. Purple indicates the XBT reference lines and red indicates deployment in 2020 (source: 21).</figcaption></figure>

The SOOP, promoted by the JCOMM, is a network of merchant and research ships equipped with sophisticated tools and technology that allow scientists to explore ocean environments. The instrumentation usually used are:

*   XBT 19, used to collect temperature observations of the upper 1 km of the ocean (Figure 4.6). Data from the XBT drop is automatically generated, transmitted by satellite and distributed on the Global Telecommunications System (GTS) in the Binary Universal Form for the Representation of meteorological data (BUFR) format. For operational use, these messages from around the globe are decoded and stored in real-time databases by each operational centre. Approximately 20,000 XBTs are deployed annually by the scientific and operational communities;

*   CTD 20, which detects how the conductivity and temperature of the water column changes relative to depth. Conductivity is a measure of how well a solution conducts electricity and it is directly related to salinity. By measuring the conductivity of seawater, the salinity can be derived from the temperature and pressure of the same water. The depth is then derived from the pressure measurement by calculating the density of water

from the temperature and the salinity. CTD are attached
to a much larger metal frame called a rosette, which may
hold water-sampling bottles that are used to collect wa-
ter at different depths, as well as other sensors that can
measure additional physical or chemical properties;

* TSG 21 are used for measuring sea surface tem-
  temperature and sea surface salinity;

* ADCP 22 are able to measure how fast water is mov-
  ing across an entire water column, using a principle of
  sound waves called the Doppler effect;

* Research vessels and voluntary observing ships par-
  ticipate in the SOOP 23

The SOOP is directed primarily towards the continued opera-
tional maintenance and co-ordination of the XBT ship-of-op-
portunity network but other types of measurements, such as
CTD probes, are also being made. The SOOP XBT program has
been greatly impacted by the global COVID-19 pandemic. In
early 2020, the program was temporarily suspended. How-
ever, almost half of lines resumed after June 2020, and by
December 2020 there were 37 ships active on 25 lines (Figure
4.6), with 4266 profiles visible on GTS (source: 24).

4.2.2.5. Gliders

Ocean gliders are autonomous underwater vehicles that move through the water column, ascending and descending with changes in buoyancy. Observations from ocean gliders have recently become an important data source in regional ocean data assimilation systems. The gliders are reusable and can be remotely controlled, making them a relatively cost-effective method for collecting repeated subsurface ocean observations. They also allow data acquisition in severe weather conditions. Equipped with a variety of sensors, the gliders are designed to measure ocean temperature, salinity and current profiles. Furthermore, the unique design of the gliders enables them to move horizontally through the water while collecting vertical profiles.

The OceanGliders program coordinates 27 nations' efforts, in-
cluding 76 national and institutional glider programs (Figure
4.7). Despite the difficult context of Covid-19 restrictions, the
OceanGliders program was able to operate over 200 gliders

16. https://www.ocean-ops.org/board

17. https://www.ocean-ops.org/board

18. https://www.ocean-ops.org/board

19. https://www.aoml.noaa.gov/phod/goos/xbt_network/

20. https://oceanexplorer.noaa.gov/facts/ctd.html

21. https://www.aoml.noaa.gov/phod/tsg/background.php

22. https://oceanexplorer.noaa.gov/technology/acoust-doppler/acoust-doppler.html#:~:text=An%20acoustic%20Doppler%20current%20profiler,physical%20properties%20of%20the%20ocean.

23. https://www.ocean-ops.org/sot/soop/

24. https://public.wmo.int/en/media/news/ocean-observ-
ing-system-report-card-2020

<figure><img src="image_76.png" /></figure>
<figure><img src="image_77.png" /><figcaption>**Figure 4.7.** Active gliders in 2020-2021 (source: 🌐²⁶).</figcaption></figure>

in 2020 (source: 🌐²⁶). Most of the glider groups share their real-time data via the GTS network.

### 4.2.2.6. HF radars

HF radar systems measure the speed and direction of ocean surface currents in real time in coastal areas. They utilise high frequency radio waves for performing such measurements: a pair of radar antennas are positioned on shore and can measure surface currents (over 1-2 m in the water column) up to 200 km offshore with a resolution spanning from 500 m to 6 km depending on the radar frequency (²⁷). Figure 4.8 shows a sketch (adapted from Mantovani et al., 2020) of mutual functioning of a pair of antennas - Radar A and Radar B: they measure the radial components (vector in blue from Radar A and vector in green from Radar B) that may

be used to compute total velocity inside each discrete cell (vector in orange). This technology is increasingly used in many applications to support downstream services for coast guard search and rescue activities, oil spill emergencies, water quality monitoring and marine navigation. Nevertheless, they are extremely useful for validating coastal models as well as assimilating OOFS at regional scale.

At international level, the GHFRN has been established as part of the GEO to promote high-frequency radar technology for scientific and operational activities along the coast. Roarty et al. (2019) include an updated list of countries and organisations providing surface current information to the GHFRN. Figure 4.9 shows the global distribution of HF radar stations organised within the three regions of the ITU.

<figure><img src="image_78.png" /><figcaption>**Figure 4.8.** Concept of surface current derivation from a two HF radar site network (adapted from Mantovani et al. 2020).</figcaption></figure>

25. https://www.oceangliders.org/

26. https://public.wmo.int/en/media/news/ocean-observing-system-report-card-2020

27. https://tidesandcurrents.noaa.gov/hfradar/

<figure><img src="image_79.png" /></figure>
<figure><img src="image_80.png" /><figcaption>**Figure 4.9.** Global distribution of HFR stations: in green, stations that share their data with global data providers; in red, those that are private and do not share their data (Roarty et al., 2019).</figcaption></figure>

An example of an operational HF radar network is provided by that one managed by Puertos del Estado, operating in Spain, to monitor coastal and harbour zones. Figure 4.10 shows on the left the current operational HF radar network: selecting one of the regions in the red boxes - for example the Ebro Delta, on the right - the user may visualise the animation of the measurements collected during the reference observing period. Data may be accessed through the EMODnet Physics webportal.

<figure><img src="image_81.png" /><figcaption>**Figure 4.10.** An example of HF radar network: the case of the Ebro Delta monitored by Puertos del Estado (Spain) (source: 28).</figcaption></figure>

### 4.2.2.7. Marine Mammals CTDs

Marine mammal CTD data are very important for ocean modelling and sea ice verification in high latitudes, particularly in the marginal sea ice zone. Since 2004, several hundred thousand profiles of temperature and salinity have been collected by instrumented animals (Figure 4.11). The use of elephant

28. http://www.puertos.es/

<figure><img src="image_82.png" /><figcaption>**Figure 4.11.** Elephant seal with CTD tag ©JB Pons, in C. Guinet, 2018, CEBC/CNRS (available at 30).</figcaption></figure>

seals has been particularly effective to sample the Southern Ocean and the North Pacific. These hydrographic data have been assembled in quality controlled databases that can be accessed through the MEOP consortium (30).

Currently, the MEOP data portal distributes three different databases:

* the MEOP-CTD database: quality-controlled CTD profiles;

* the MEOP-SMS database: submesoscale-resolving high density CTD data;

* the MEOP-TDR database: high spatial density temperature/light data.

Real-time marine mammal CTD data are uploaded to the GTS as shown at 31.

### 4.2.2.8. Autonomous underwater vehicles

An AUV is a self-propelled, unmanned, untethered, underwater vehicle capable of carrying out simple activities with little or no human supervision. Reasons for employing AUV range from the ability to obtain superior data quality (for example, obtaining high-resolution maps of the deep seafloor) to establishing a pervasive ocean presence (for example, using many small AUV to observe oceanographic fields) (Bellingham, 2009).

### 4.2.2.9. List of most relevant international in-situ data providers

Providers of international in-situ observations to be used for assimilation/validation are listed in Table 4.2.

29. https://www.cebc.cnrs.fr/wp-content/uploads/pub-lipdf/2019/GC124006.pdf

30. http://www.meop.net/

31. http://www.meop.net/meop-portal/ctd-srdl-technology.html

<figure><img src="image_83.png" /></figure>
<table><thead><tr><td>Provider</td><td>Description</td><td>Website</td></tr></thead><tbody><tr><td>WOD</td><td>World Ocean Database provides uniformly formatted, quality controlled, publicly available ocean profiles</td><td>https://www.ncei.noaa.gov/products/world-ocean-database</td></tr><tr><td>Argo</td><td>Argo provides data access to Global Data Assembly Centres in Brest (France) and in Monterey (USA)</td><td>https://argo.ucsd.edu/about/status/</td></tr><tr><td>Copernicus Marine Service</td><td>Copernicus Marine Service through the INS TAC for the operational provisioning of near real time and reprocessed datasets used by the MFCs for assimilation and validation</td><td>https://marine.copernicus.eu/</td></tr><tr><td>SeaDataNet</td><td>SeaDataNet infrastructure, provides aggregated datasets (ODV collections of all unrestricted SeaDataNet measurements of temperature and salinity by sea basins) and climatologies (regional gridded field products) based on the aggregated datasets and data from external data sources such as the CORA and the WOD for all the European sea basins and the Global Ocean</td><td>https://www.seadatanet.org/</td></tr><tr><td>EMODnet</td><td>European Marine Observation and Data Network is a long-term, marine data initiative funded by the European Maritime and Fisheries Fund which, together with the Copernicus space programme and the Data Collection Framework for fisheries, implements the EU's Marine Knowledge 2020 strategy. EMODnet Physics provides a single point of access to validated in-situ datasets, products and their physical parameter metadata of European Seas and global oceans. More specifically, time series and datasets are made available, as recorded by fixed platforms (moorings, tide gauges, HF radars, etc.), moving platforms (Argo, Lagrangian buoys, ferryboxes, etc.) and repeated observations (CTDs, etc.)</td><td>https://www.emodnet.eu/</td></tr></tbody></table>

<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

Copernicus Marine Service

<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>

EMODnet

<caption>**Table 4.2.** List of most relevant international in-situ data providers.</caption>

### 4.2.3. Description of satellite observational oceanographic data

Satellite altimetry is one of the most important techniques for operational oceanography. Figure 4.12, adapted from International Altimetry Team (2021), shows an overview of the radar altimetry constellation and timeline as available from early 90' and with a projection beyond 2030: it demonstrates how altimetry can be considered as a well-established Earth observation platform from space and its evolution contributes to scientific advances in ocean dynamics. Figure 4.12, in particular, reports the main international missions operational temporal framework: before 2020, we have a number of satellites that are not operational anymore (in orange) but

that provide a huge and valuable source of historical observations. Then there are modern operational satellites for the provisioning of near real time altimetry data (in yellow): for some of them, the data provider is also able to report the degraded quality period. New missions (e.g., SWOT, Sentinel6) are planned to be launched starting from 2022. These missions should be able to provide very high quality and high resolution altimetry products (light yellow to green). Some of the operational satellite platforms are also part of the DUACS (in dark blue): these consist of a multi-mission merged dataset for measuring, in particular, ocean mesoscale dynamics (more details are also available at 32).

<figure><img src="image_90.png" /></figure>

<footnote n="32">32. https://duacs.cls.fr/</footnote>
<figure><img src="image_91.png" /><figcaption>**Figure 4.12.** Altimetry satellites timeline (adapted from International Altimetry Team, 2021).</figcaption></figure>

<figure><img src="image_92.png" /></figure>
Satellite altimetry has substantially advanced understanding of the oceans by providing unprecedented observations of the surface topography at scales larger than 200 km, thus increasing our knowledge of global ocean circulation from the role of mesoscale eddies in shaping ocean circulation to the global sea level rise. The following sections describe the variables measured by satellites.

#### 4.2.3.1. Satellite sea surface temperature

The SST is another important data source for ocean data assimilation and monitoring oceanic conditions. Since the beginning of operational satellite SST observations in 1981, the number and diversity of sensors have increased dramatically and are still evolving (O'Carroll, et al. 2019). A combination of infrared - onboard both LEO and geostationary orbit platforms - and passive microwave (LEO only) radiometers provide a comprehensive global SST coverage to meet the minimum data specification to be used in operational ocean models (as defined by GODAE in Bell et al., 2009).

Most satellite SST observations assimilated into ocean prediction systems are processed in accordance with guidelines and formats specified by the GHRSSST (Donlon et al., 2009); an example of a multi-product ensemble is shown in Figure 4.13.

<figure><img src="image_93.png" /><figcaption>**Figure 4.13.** Example of SST maps as provided by GHRSSST multi-product ensemble (source: Ⓔ³³)</figcaption></figure>

33. https://www.ghrsst.org/latest-sst-map/

GHRSSST formatted products supply SST data either in satellite swath coordinates level 2 preprocessed (L2P) or level 3 composite (L3) gridded netCDF4 format files. L2P and L3 data products provide satellite SST observations together with a measure of uncertainty for each observation in a common GHRSSST netCDF format (GHRSSST Science Team, 2012). Auxiliary fields are also provided for each pixel as dynamic flags to filter and help interpret the SST data. These data are ideal for data assimilation systems or as input to analysis systems. Gridding a single L2P file produces an “uncollated” L3 file (L3U). Multiple L2P files are gridded to produce either a “collected” L3 file (L3C) from a single sensor or a “super-collated” L3 file from multiple sensors (L3S) (source: Ⓔ³⁴).

There are a wide range of satellite SST products in L2P or L3 format provided by various GHRSSST regional and data assembly centres. The following is a list of SST products from different satellite sensors that are common to many ocean prediction systems:

* Passive Microwave Radiometers on LEO polar-orbiting satellites provide low spatial resolution SST at around 1 mm depth, with global coverage of the Earth at the equator up to twice daily and more frequently at higher latitudes. SST products obtained from passive microwave radiometers are effective at detecting ocean front variability in regions at least 50 km from land, under either clear or cloudy conditions but not precipitation. Most ocean prediction systems assimilate SST observations at ~25 km spatial resolution from the AMSR2 aboard the JAXA polar-orbiting satellite. These data are made available via the JAXA EORC (SCOPE) and Remote Sensing Systems (RSS).

* Infrared radiometers on LEO satellites provide high spatial resolution SST at around 10 micrometer depth, with global coverage of the Earth under clear sky conditions up to twice daily at the equator and more frequently at higher latitudes. SST products commonly used are measured by the Advanced Very High-Resolution Radiometer (AVHRR) instrument flown by the Meteorological Operational satellite (MetOp) series of polar-orbiting environmental satellites launched by the ESA and operated by the EUMETSAT. Two types of AVHRR SST products used in ocean prediction systems are: 1) the 1.1 to ~4 km spatial resolution FRAC AVHRR L2P and 2) the 4.4 to ~18 km resolution GAC AVHRR L2P, produced by the OSI SAF within EUMETSAT (EPO), OSPO (OSP), and NAVOCEANO.

34. https://www.ghrsst.org/ghrsst-data-services/products/

35. https://www.eorc.jaxa.jp/en/

36. http://www.remsss.com/missions/amsr/

37. http://www.osi-saf.org/?q=content/sst-products

38. https://www.ospo.noaa.gov/

<figure><img src="image_94.png" /></figure>
The NAVOCEANO FRAC and GAC AVHRR L2P SST data are made available under the MISST (⊕39) project sponsorship by the ONR and the PO.DAAC (⊕40) operated by the NASA JPL. The newest NOAA JPSS satellites (Suomi-NPP and NOAA-20) are now equipped with the VIIRS sensors, that have a wide range of infrared channels, and provide SST at 0.75 km to 1.5 km resolution. In order to facilitate ingestion into real-time operational ocean systems, the VIIRS level 3 Uncollated (L3U) data are produced by the NOAA OSPO (⊕41), and publicly available from NOAA OceanWatch (⊕42) and PO.DAAC.

* Infrared radiometers on geostationary satellites above the equator provide high spatial (2~5 km) and temporal (10~60 minute) resolution SST observations over a fixed geographic region. There are several GEO satellites distributed around the equator and operated by different agencies (i.e. ESA, ISRO, NOAA, JMA, JAXA, KMA and CMA); they provide high temporal resolution SST that can improve clear-sky masking by using temporal information to separate the effects of faster moving clouds and other atmospheric features from the slower evolving SST fields (O'Carroll et al., 2019). One example is the AHI sensor of the JMA geostationary satellite "Himawari-8", which allows relatively high-frequency measurement of SST (every 10 minutes with horizontal resolution ~2 km) in a wide area of the Western Pacific (Kurihara et al., 2016). Data are made available by JAXA (⊕43), NOAA (⊕44) and the Australian Bureau of Meteorology via the National Computational Infrastructure (⊕45).

Surface diurnal warming events occur in ocean regions of high solar radiation, clear skies, and calm seas. They are more common in the tropics (Zhang et al., 2016) but have also been observed at high latitudes (Eastwood et al., 2011). The warming events produce near-surface thermal gradients that create daytime near-surface or warm-layer temperatures up to 2-4°C warmer than nighttime (Donlon et al., 2002). Some operational centres exclude daytime satellite SST observations to reduce the diurnal warm bias and only use night-time satellite SST to assimilate into ocean analyses and forecast models. Most GHRSSST L2P or L3U format SST data are cor-

rected for bias by subtracting the SSES bias value associated with each SST value (GHRSSST Science Team, 2012), derived by data providers using recent matchups with SST observations from drifting buoys and tropical moorings (Petrenko et al., 2016) that produce SST estimates at around 0.2 m depth.

#### 4.2.3.2. Satellite Altimeter

The main parameter that can be derived from satellite altimeters is SLA relative to a reference mean dynamic topography. SLA is fundamental for sea level monitoring and ocean data assimilation. Two freely available common data sources for real-time altimetry data retrieval are the RADS - which was developed by the DEOS and the NOAA Laboratory for Satellite Altimetry (Naeije et al., 2000; Scharroo, 2012) - and the Copernicus Marine Service (Figure 4.14).

The DEOS is building and developing the RADS database that incorporates validated and verified altimetry data products. The database is consistent in accuracy, correction, format and reference system parameters. The capability of such a database has attracted users with less satellite altimeter expertise. Currently, RADS enables users to extract the data from several present and past satellite altimeter missions like GEOSAT, ERS1, ERS2, ENVISAT, TOPEX/Poseidon (T/P), JASON1, JASON2, JASON3, CRYOSAT2, SENTINEL-3A, and SARAL (⊕46).

The Level 3 SLA product from Copernicus Marine Service is another open accessible data source for SLA. It shares many of the most useful features of the RADS service, including adaptation to changes in the available satellite fleet and

<figure><img src="image_95.png" /><figcaption>**Figure 4.14.** Global ocean along track sea level anomaly (source: ⊕47).</figcaption></figure>

39. https://www.nopp.org/projects/multi-sensor-improved-sea-surface-temperature-misst

40. https://podaac.jpL.nasa.gov/

41. https://www.ospo.noaa.gov/

42. https://coastwatch.noaa.gov/cw/satellite-data-products/sea-surface-temperature.html

43. http://suzaku.eorc.jaxa.jp/GHRSSST/

44. https://coastwatch.noaa.gov/cw/satellite-data-products/sea-surface-temperature/acspo-ahi.html

45. https://nci.org.au/

46. http://rads.tudelft.nl/rads/data/authentication.cgi

47. https://datastore.cls.fr/catalogues/global-ocean-along-track-sea-level-anomalies/

<figure><img src="image_96.png" /></figure>
maintaining homogeneity. Although superficially RADS and Copernicus Marine Service seem providing the same type of SLA observation they are not identical and a detailed explanation of differences is non-trivial, as the RADS data includes many of the corrections used by Copernicus Marine Service, as well as the corrections applied in its own processing. Users are encouraged to explore the differences between these two data streams and choose the suitable satellite altimeter data source for their own data assimilation system.

#### 4.2.3.3. Satellite Sea Surface Salinity

Measuring SSS from space is a relatively recent technique that relies on L-band radiometry (which has evolved to a point where useful information is provided every few days). Satellite SSS offers the advantages of global coverage and the ability to capture space and time scales not afforded by in-situ platforms such as vessels, moorings, and Argo profiling floats. Figure 4.15 shows a year of satellite SSS products from the ESA's SMOS and NASA Aquarius and SMAP missions. It is worth noting that regions of high variability of >0.2 psu - including coastal oceans, western boundary currents, the Indonesian Seas, and the Southern and Arctic Oceans - are either not sampled or poorly sampled by Argo (Vinogradova et al., 2019).

<figure><img src="image_97.png" /><figcaption>**Figure 4.15.** Variability in space-borne sea surface salinity during one year (colors) superimposed with locations of currently operational Argo floats (white dots) from Vinogradova et al. (2019).</figcaption></figure>

Level 3 observations (L3 - provided on a grid but with no in-filling) with various temporal and spatial averaging from the SMOS, Aquarius, and SMAP satellites are available, as are level 2 data (L2; SSS values at the native swath resolution). For SMOS and Aquarius, L3 products are available daily, with separate files for the ascending and descending parts of the orbit. The products used are from the LOCEAN (48) and the JPL (49) respectively for SMOS and Aquarius. While there is

48. www.catds.fr

49. https://podaac.jpl.nasa.gov/

<figure><img src="image_98.png" /><figcaption>**Figure 4.16.** Example of satellite-based product for sea ice extension in the Northern Hemisphere (source: 50).</figcaption></figure>

a daily L3 SMAP product, it is based on observations from an 8-day period that would require a complicated observation operator in the data assimilation.

The availability of SSS from SMOS, Aquarius and SMAP has enabled ocean forecast validation (e.g., Vinogradova et al., 2014; Martin, 2016). In recent years, efforts have been put into assimilating satellite SSS data, which is challenging for several reasons. Largely, these are related to the magnitude of errors in the data, particularly in the SSS products needed for operational-style forecasting systems that are required at high temporal resolution (Martin et al., 2019). Quality control of satellite SSS has proved to be a very important process for ocean data assimilation.

#### 4.2.3.4. Satellite sea ice

The sea ice concentrations from Nimbus-7 SMMR sensor and DMSP SSM/I passive microwave data, are accessible from the NASA NSIDC DAAC (51) (Figure 4.16). This sea ice concentration dataset is generated from brightness tem-

50. https://earth.gsfc.nasa.gov/cryo/data/current-state-sea-ice-cover

51. https://doi.org/10.5067/8GQ8LZQVL0VL

<figure><img src="image_99.png" /></figure>
perature data and is designed to provide a consistent time
series of sea ice concentrations spanning the coverage of
several passive microwave instruments. The data are pro-
vided in the polar stereographic projection at a grid cell
size of 25 x 25 km. This is then interpolated to 10 km reso-
lution, level 3 composite of SSMIS level 2 data, on a polar
stereographic grid (ersh). Daily files are available within 24-
48 hours after last satellite acquisition.

The same satellite sea ice concentration data originating
from NSDIS SSM/I aboard the DMSP series of polar-orbiting
sun-synchronous satellites, are provided by the OSI SAF
(ersh). The global daily sea ice concentration is processed by
OSI SAF at 10 km resolution as level 3 composites of SSMIS
level 2 data on a Polar Stereographic grid. Northern Hemi-
sphere and Southern Hemisphere daily files are available
within 6 hours after last satellite acquisition.

4.2.3.5. Ocean Colour

Ocean colour measurement consists of detecting spectral
variations in the water-leaving radiance (or reflectance),
which is the sunlight backscattered out of the ocean after in-
teraction with water and its constituents (Groom et al., 2019).
This is a very significant measurement for the monitoring of
ocean water quality, ocean acidification, or to understand
the global carbon cycle, apart from using it for assimilation

and validation. In the open ocean, the signal is largely influ-
enced by the presence of phytoplankton and dissolved or-
ganic matter; in coastal waters, it is also influenced by resus-
pended particulate matter and river runoff that transports
other kinds of anthropogenic particulate. In the framework
of the Copernicus Marine Service, two types of products are
delivered by the OC TAC (sh54):

*   CHL is the phytoplankton chlorophyll concentration. For the global and regional seas, OC TAC selected the state-of-the-art product algorithm on the basis of optical characteristics of the basin and round robin procedure. For the regional seas, daily chlorophyll fields are produced by applying two different algorithms for open ocean (Case I) and coastal waters (Case II). The data are then merged into a single chlorophyll field providing a regional product with an improved accuracy of estimates in coastal waters.

* The OPTICS product includes all other variables retrieved from ocean colour sensors: IOP, such as absorption and scattering, the diffuse attenuation coefficient of light at 490 nm (Kd490), Secchi depth (transparency of water), spectral Rrs, PAR, CDOM, and the SPM.

Figure 4.17 shows an example of chlorophyll concentration at global scale from the MODIS Aqua satellite.

<figure><img src="image_100.png" /><figcaption>**Figure 4.17.** MODIS Aqua chlor_a seasonal composite for Spring 2014 (source: sh55).</figcaption></figure>

52. https://nsidc.org/data/nsidc-0081

53. http://www.osi-saf.org/?q=content/sea-ice-products

54. https://marine.copernicus.eu/about/producers/oc-tac

55. https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/

<figure><img src="image_101.png" /></figure>
<figure><img src="image_102.png" /><figcaption>**Figure 4.18.** Sentinel-3 SRAL significant wave height Level-2 global map (source: 56).</figcaption></figure>

#### 4.2.3.6. Significant Wave Height

The SWH (or Hs) is the average wave height (from trough to crest) of the highest third (33.33%) of the waves in a given sample period. The Sentinel-3 mission is able to monitor wave heights from 0 to 20 m. The marine sea state SWH product is a critical product for all maritime safety and rescue operations (from 57).

Figure 4.18 shows an example of SWH for the global ocean from Sentinel-3A measurements.

#### 4.2.3.7. Providers of satellite data

Providers of satellite observations to be used for assimilation/validation are listed in Table 4.3.

#### 4.2.4. Bathymetry

The term “bathymetry” refers to the ocean’s depth relative to the sea level. It is an important element in any ocean model, since it allows us to represent the geographical and topographical peculiarities of the sea floor. It has a strong influence on the circulation, notably its barotropic and depth-integrated features, in particular (but not only) at sills and straits,

<figure><img src="image_103.png" /><figcaption>**Figure 4.19.** An example of a bathymetric dataset: the EMODnet bathymetry (source: 58).</figcaption></figure>

on coastal and in shelf seas. For this reason, its accuracy may determine the goodness of the ocean model, although there are issues of smoothing and grid mislocation that need to be considered and solved by using ad hoc spatial analysis.

<figure><img src="image_104.png" /></figure>

<footnote n="56">56. https://www.eumetsat.int/new-S3-sral-wave-products</footnote>

<footnote n="57">57. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview/geophysical-measurements/significant-wave-height</footnote>

<footnote n="58">58. https://www.emodnet-bathymetry.eu</footnote>
<table><thead><tr><td>Provider</td><td>Description</td><td>Website</td></tr></thead><tbody><tr><td>Copernicus Marine Service</td><td>Copernicus Mawrine Service through the SL, SST, OC, WAVE TACS for the operational provisioning of near real time and reprocessed datasets used by the Monitoring and Forecasting Centres (MFCs) for assimilation and validation</td><td>Copernicus Marine Service<br>https://marine.copernicus.eu/</td></tr><tr><td>GHRsst</td><td>The Group for High-Resolution Sea Surface Temperature (SST) (GHRsst) provides a new generation of global high-resolution (<10km) SST products to the operational oceanographic, meteorological, climate and general scientific community</td><td>GHRsst<br>https://www.ghrsst.org/</td></tr><tr><td>AVISO++</td><td>AVISO++ provides altimeter data</td><td>AVISO+<br>https://www.aviso.altimetry.fr/en/home.html</td></tr><tr><td>EUMETSAT</td><td>EUMETSAT is the European operational satellite agency for monitoring weather, climate and the environment from space. In particular, it provides SST and altimeter data</td><td>EUMETSAT<br>https://www.eumetsat.int/</td></tr><tr><td>NOAA NSIDC</td><td>NOAA National Snow and Ice Data Centre provides sea ice concentration in the polar region</td><td>NSIDC<br>https://nsidc.org/</td></tr></tbody></table>

<figure><img src="image_105.png" /></figure>

<figure><img src="image_106.png" /></figure>

<figure><img src="image_107.png" /></figure>

<figure><img src="image_108.png" /></figure>

<figure><img src="image_109.png" /></figure>

<caption>**Table 4.3.** List of most relevant international satellite data providers.</caption>

A bathymetric dataset needs to be interpolated onto the model's grid. Pre-processing of the bathymetric fields should be necessary for numerical reasons: since bathymetry datasets are usually finer than the model grid, they may need to be smoothed before inserted on the model grid. Effective resolution and vertical coordinates of the ocean model could also constrain the smoothness of the bathymetry.

Figure 4.19 shows an example of a bathymetric dataset as provided by EMODnet bathymetry.

Table 4.4 includes a list of public providers of bathymetric datasets (Marks and Smith, 2006).

### 4.2.5. Atmospheric forcing

Typically, NWP systems provide atmospheric surface forcing fields to OOFS in order to compute water, heat, and momentum fluxes. Such fields may be also supplemented by real-time or near real-time observations and other averaged datasets including climatology. Certainly, in a more complex modelling framework, an ad hoc atmospheric model can be developed at the same

resolution of the ocean model in order to provide high resolution atmospheric fields (coupled systems, see Chapter 10 for further details).

In general, typical surface data input required by an OOFS that is provided by an NWP model includes:

* Sea ice coverage;

* Downward surface longwave radiation;

* Upward surface longwave radiation;

* Downward surface shortwave radiation;

* Upward surface shortwave radiation;

* Dewpoint depression at 2 m;

* Surface latent heat;

* Mean sea level pressure;

* Surface sensible heat;

* Specific humidity at 2 m;

* Air temperature at 2 m;

* Cumulative precipitation rates;

* Zonal and meridional wind components and wind speed at 10 m (or surface wind stresses);

* Short-wave radiation heat flux penetrating through ice;

<figure><img src="image_110.png" /></figure>
<table><thead><tr><td>Product</td><td>Description</td><td>Provider</td></tr></thead><tbody><tr><td>DBDB2</td><td>Digital Bathymetric DataBase at 2 min by 2 min uniform grid global bathymetry and topography data developed for the ocean model. It was developed by the Naval Research Laboratory</td><td>https://www7320.nrlssc.navy.mil/DBDB2_WWW/</td></tr><tr><td>ETOPO1</td><td>1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. It was built from numerous global and regional data sets. Historic ETOPO2v2 and ETOPO5 global relief grids are depreciated but still available</td><td>http://www.ngdc.noaa.gov/mgg/global/</td></tr><tr><td>GEBCO</td><td>Gridded Bathymetry Data for the World's oceans at 15 arc-second resolution. It operates under the joint auspices of the IHO and the UNESCO IOC</td><td>https://www.gebco.net/</td></tr><tr><td>SRTM+</td><td>Global bathymetry and topography. SRTM15+ is the last version at 15 arc-second resolution, built upon the latest compilation of ship-board sounding and satellite-derived predicted depths. V2.0 is part of the last release of GEBCO_2020 (Tozer et al., 2019)</td><td>http://topex.ucsd.edu/marine_topo/</td></tr><tr><td>EMODnet Bathymetry</td><td>It is part of the EMODnet project, funded by the European Commission, which brings together marine data into interoperable, continuous and publicly available bathymetric dataset for all the maritime basins in European waters and for the global ocean</td><td>https://www.emodnet-bathymetry.eu/</td></tr></tbody></table>

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /><figcaption>**Table 4.4.** Bathymetric dataset products and providers.</figcaption></figure>

EMODnet

* Ice freezing/melting heat flux;

* Zonal and meridional ice stress on ocean;

* Sea-Ice basal salt flux.

The above list is not exhaustive and inputs can vary based on the needs of the OOFs. For example, it can be used SST from the OOFS along with the air temperatures at 2 m to calculate sensible heat flux instead of using that provided by NWP. More details on thermodynamic and momentum forcing of the ocean can be found in Barnier (1998), Barnier et. al. (1995), Josey et al. (1999).

Figure 4.20 shows an example of surface forcing atmospheric fields from the ECMWF IFS.

A list of global NWP systems is provided in Table 4.5.

<figure><img src="image_115.png" /></figure>
Base time: Mon 13 Jun 2022 00 UTC Valid time: Mon 13 Jun 2022 06 UTC (+6h) Interval (hr) : 6 Area : Global

<figure><img src="image_116.png" /><figcaption>**Figure 4.20.** An example of surface forcing fields: rain and mean sea level pressure at global scale from ECMWF (source: 59).</figcaption></figure>

<table><thead><tr><td>Dataset</td><td>Description</td><td>Provider</td></tr></thead><tbody><tr><td>GFS</td><td>Global Forecast System, produced by the National Centers for Environmental Prediction (NCEP), provides analysis and forecast atmospheric fields for the global ocean at the resolution of about 28 km</td><td>https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forecast-system-gfs<br>National Centers for Environmental Information</td></tr><tr><td>NAVGEM</td><td>Navy Global Environmental Model runs by the United States Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC)</td><td>https://www.usno.navy.mil/FNMOC/meteorology-products-1m<br>ECMWF</td></tr><tr><td>ECMWF IFS and ERA5</td><td>European Center for Medium range Weather Forecasting that provides reanalysis, analysis and forecast atmospheric fields at medium, extended, and long range</td><td>https://www.ecmwf.int/<br>Met Office</td></tr><tr><td>Met Office UK</td><td>United Kingdom Meteorological Office that produces the Unified Model, a numerical model of the atmosphere used for both weather and climate applications</td><td>https://www.metoffice.gov.uk/<br>Environment Canada</td></tr><tr><td>GEM</td><td>Global Environmental Multiscale model, an integrated forecasting and data assimilation system developed in the Recherche en Prévision Numérique (RPN), Meteorological Research Branch (MRB), and the Canadian Meteorological Centre (CMC)</td><td>https://collaboration.cmc.ec.gc.ca/</td></tr></tbody></table>

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

<figure><img src="image_121.png" /></figure>

<figure><img src="image_122.png" /></figure>

<figure><img src="image_123.png" /><figcaption>**Table 4.5.** Atmospheric forcing products and providers.</figcaption></figure>

59. https://www.ecmwf.int/

<figure><img src="image_124.png" /></figure>
<figure><img src="image_125.png" /><figcaption>**Figure 4.21.** An example of river runoff discharge data provider: worldwide distribution of stations contributing to GRDC (source: 👤 60).</figcaption></figure>

<figure><img src="image_126.png" /><figcaption>**Figure 4.22.** An example of river runoff discharge (monthly data) time series from GRDC related to Ceatal Izmail station (Romania) that monitors the Danube basin (source: 👤 61).</figcaption></figure>

60. https://www.bafg.de/GRDC/EN/Home/homepage_node.html

61. https://www.bafg.de/GRDC/EN/Home/homepage_node.html

<figure><img src="image_127.png" /></figure>
<table><thead><tr><th>Dataset</th><th>Description</th><th>Provider</th></tr></thead><tbody><tr><td>GRDC</td><td>Global Runoff Data Base, built on an initial dataset collected in the early 1980s from the responses to a WMO request to its member countries to provide global hydrological information</td><td>GRDC https://www.bafg.de/GRDC/EN/01_GRDC/13_dtbse/database_node.html</td></tr><tr><td>Dai and Trenberth</td><td>Dai and Trenberth Global River Flow and Continental Discharge Dataset contains time series of all available monthly river flow rates observed at the farthest downstream station for the world's largest 925 rivers, plus long-term mean river flow rates and continental discharge into the individual and global oceans, produced originally by Dai and Trenberth (2002) and Dai et al. (2009) and Dai (2021)</td><td>NCAR ☀ UCAR https://rda.ucar.edu/datasets/ds551.0</td></tr><tr><td>EFAS</td><td>European Flood Awareness System developed and operational within the Copernicus Emergency Management Service. It provides grided modelled daily hydrological time series forced by meteorological observations. It includes river discharge, soil moisture for three soil layers and snow water equivalent</td><td>Emergency Management https://www.efas.eu/</td></tr><tr><td>GLOFAS</td><td>Global Flood Awareness System, operational within the Copernicus Emergency Management Service. It couples state-of-the art weather forecasts with a hydrological model and with its continental scale set-up, providing downstream countries with information on upstream river conditions as well as continental and global overviews</td><td>Emergency Management https://www.globalfloods.eu/</td></tr><tr><td>EMODnet Physics</td><td>EMODnet Physics gathers, harmonises and makes available near real time river runoff and in-situ river runoff trends (monthly and annual means), accessible through the website with MapViewer controllers</td><td>EMODnet https://map.emodnet-physics.eu/</td></tr></tbody></table>

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

<figure><img src="image_135.png" /></figure>

<caption>**Table 4.6.** River data providers.</caption>

### 4.2.6. Land forcing

Rivers represent the natural element connecting land and ocean through the coastline. They impact both coastal and basin-wide circulation and dynamics through net freshwater flux; additionally, they are responsible for biotic diversity and eutrophication, particularly in coastal waters.

Water discharges, nutrients, and organic materials represent sources of freshwater and biogeochemical fluxes for an OOFs, and we have to account for them once we set a numerical model. This kind of data may come from observations or from other models (hydrological or biogeochemical models). In particular, information about discharge, and possibly also salinity and temperature if available, should be provided for the river mouth at given coordinates.

As an example, in Figure 4.21 is shown the distribution at global scale of stations that operated/are operating in a certain temporal period contributing to the GRDC. Once the user selects one of the stations, the web service returns the water discharge timeseries (Figure 4.22) allowing to download and integrate it as an input dataset in the ocean model setup.

Table 4.6 provides a list of international databases for river data.

Below are listed some other initiatives for handling freshwater inputs with focus on icebergs and R&D project:

* Altiberg is a database for small icebergs (< 3km in length), detected by altimeters using the high-resolution waveforms (Tournadre et al., 2016), 62;

62. http://cersat.ifremer.fr/user-community/news/item/473-altiberg-a-database-for-small-icebergs

<figure><img src="image_136.png" /></figure>
* BRONCO stands for “Benefits of dynamically modelled river discharge input for ocean and coupled atmosphere-land-ocean systems”: it is a Service Evolution Project run in the framework of Copernicus Marine Service to improve and standardise input of river discharge into global, regional and coastal models, 🌐⁶³;

* LAMBDA stands for Land-Marine Boundary Development & Analysis: it is another Service Evolution Project run in the framework of Copernicus Marine Service. It aims at improving the Copernicus Marine Service MFCs thermohaline circulation in coastal areas by better characterization of the land-marine boundary conditions, 🌐⁶⁴.

### 4.2.7. OOFS fields as input for downscaling

An OOFS may be set also using information from other OOFSSs: this is the case of the so-called nesting models (for major details see Section 5.4.4). For example, the GLO-PHY - herein referred to as parent model - provides lateral open boundary conditions to the Mediterranean Sea Forecasting System (MedFS) - herein referred to as child model. Both systems are part of the Copernicus Marine Service catalogue. Figure 4.23 shows a typical ocean field at global scale from GLO-PHY - in this case, we display sea surface temperature forecast product. The parent model provides temperature, salinity, sea surface height, zonal and meridional velocity components to the Mediterranean Sea through 3 open boundaries located in the Atlantic Ocean. Ocean fields from the parent model are spatially and temporally interpolated over the open boundary sections and provided to the ocean circulation model of the child domain. Figure 4.24 shows as example the Mediterranean Sea surface currents forecast product after integrating the numerical model accounting for the GLO-PHY ocean fields as lateral open boundary conditions.

For major details about the setup of both systems, please refer to the Copernicus Marine Service web pages dedicated to each product.

### 4.2.8. Climatology from observations

To describe the general oceanographic conditions at different time scales and spatial resolutions, climatological fields computed from observations can be used. They are defined as mean values of a certain variable in a certain period (e.g. month, season, etc.). They may be used for creating initial and/or boundary conditions for an ocean model, as well as validating numerical results and performing data assimilation.

Since observations are irregularly distributed in space, an objective analysis (Chang et al. 2009) is needed in order to

63. https://www.mercator-ocean.fr/en/portfolio/bronco-2

64. http://www.cmems-lambda.eu/

<figure><img src="image_137.png" /><figcaption>**Figure 4.23.** The GLO-PHY sea surface temperature on 26 May 2022 (source: 🌐⁶⁵ through the Ocean Viewer 🌐⁶⁶).</figcaption></figure>

<figure><img src="image_138.png" /><figcaption>**Figure 4.24.** The MedFS sea surface currents on 26 May 2022 (source: 🌐⁶⁷ through the Ocean Viewer 🌐⁶⁸).</figcaption></figure>

produce spatially gridded dataset that can be easily used by a numerical model. Numerical model results, being gridded, can be easily aggregated in time to produce a climatological field to be used as initial or boundary condition.

Climatologies may be also computed from NWP products to modify or to formulate ocean surface fluxes using mean momentum conditions from a reanalysis product (e.g., ECMWF ERA5, etc.) superposed with variability from the NWP fields. Additionally, observations such SSS and SST may be adopted for supplementing climatological data for surface flux relaxation to control model drifts. Finally, climatologies may be computed also from other ocean models to provide lateral open boundary conditions ( numerics and methods will be presented in Chapter 5).

Figure 4.25 provides as an example of climatology the annual sea surface temperature computed over the period 1955-2017 for the global ocean by the WOA.

Table 4.7 provides a list of international atlases.

65. https://marine.copernicus.eu/

66. https://myocean.marine.copernicus.eu/

67. https://marine.copernicus.eu/

68. https://myocean.marine.copernicus.eu/

<figure><img src="image_139.png" /></figure>
<figure><img src="image_140.png" /><figcaption>**Figure 4.25.** An example of climatology: temperature field from World Ocean Atlas Climatology (source: 69).</figcaption></figure>

<table><caption>**Table 4.7.** Climatology products and providers.</caption><thead><tr><td>Dataset</td><td>Description</td><td>Provider</td></tr></thead><tbody><tr><td>WOA</td><td>World Ocean Atlas (Boyer et al., 2019) provides climatological temperature (°C), salinity (unitless), density (kg/m3), mixed layer depth (m) and other biogeochemical parameters (for the latter, major details are provided in Chapter 9)</td><td>National Centers for Environmental Information<br>https://www.ncei.noaa.gov/products/world-ocean-atlas</td></tr><tr><td>WOD</td><td>World Ocean Database (Boyer et al., 2019), is a continuation of the Climatological Atlas of the World Ocean (Levitus, 1982) and at present represents one of the world's largest collection of uniformly formatted, quality controlled, and publicly available ocean profiles data</td><td>National Centers for Environmental Information<br>https://www.ncei.noaa.gov/products/world-ocean-database</td></tr><tr><td>SeaDataNet</td><td>SeaDataNet is a distributed Marine Data Infrastructure for the management of large and diverse sets of data deriving from in situ of the seas and oceans. It provides an online access to data on regional climatologies products - gridded fields of sea temperature and salinity - for the European seas (Arctic Sea, Baltic Sea, Black Sea, Mediterranean Sea, North Sea, North Atlantic Ocean) and for the global ocean</td><td>SeaDataNet<br>https://www.seadatanet.org/Products/Climatologies</td></tr></tbody></table>

<figure><img src="image_141.png" /></figure>

<footnote n="69">69. https://www.ncei.noaa.gov/</footnote>