**6.1. Designing seasonal outlook products and tools**

578 Risk Management – Current Issues and Challenges

across networks efficiently.

web services.

In the **forecast generation layer**, forecast products are generated from dynamical model output. This process involves statistical corrections for model biases and may involve the integration of outputs from a number of different models. Decisions about which model outputs to use will be based on the analysis of model performance over an historical period. The resulting derived forecast products are typically stored in self-describing files with additional metadata to support the clients that deliver the outlooks. By storing the generated forecasts in an accessible, metadata rich format they are easily ingestible by downstream clients, whether these are simple viewers or more complex models that use the calibrated model data as one input among many. GCM model outputs may be used to drive other models (for example hydrological models). In general metadata is preserved as data is processed and new metadata added to describe transformations. This enables downstream users of the data to understand its provenance. Metadata curation, while tedious, should be considered a best practice if data is to be made public and its use promoted. Practitioners in this domain will typically be data

scientists, statisticians, and climate scientists who work closely with forecast users.

At the **data service layer** forecast data is exposed via a data server, which makes the forecast data available using standard interfaces such as OPEnDAP[35]. At this stage, the generated outlooks are data products, not graphical products. The format of the output is not dependent on the particular dynamical model, or even that the model is dynamical: the forecast is simply a time series of gridded data with descriptive metadata. This layer is the domain of information architects and software engineers with expertise in moving data

The **product service layer** provides the means for the majority of forecast users to access the products they require, typically in the form of maps and graphs presented as images, data tables and expert commentary. Such products are developed by climate scientists and associated professionals with expertise in data visualisation, usually in close consultation with forecast users. This layer may take the form of pre-generated images and tables, or of complex applications that obtain and process data directly from the data service layer using

The use of open standards, interoperable systems and simple, clean interfaces simplify the challenge of integrating data from multiple streams into usable seasonal forecasts. Systems need to be interoperable to reduce the cost of exporting and ingesting data, a procedure which is required at all stages of the process from the modelling (where analyses must be ingested for the initialisation of models) to the product services (where potentially large volumes of image and web page requests must be serviced). The use of open standards supports this interoperability. Open standards arise in communities of practice over periods of time, and generally become enshrined in documentation and formally supported by interinstitutional bodies. They are to be preferred over the creation of new *ad hoc* formats and interfaces. Clean interfaces means that coupling between system modules should be kept to a minimum, and that system modules communicate with each other as far as possible using the standards described above. The integration of model outputs into arbitrary decisionsupport systems and downstream models is supported by providing the model output, and Agile software development methodologies allow technical development to proceed simultaneously with the gathering of user feedback and refinement of designs. They are characterised by short development cycles with clearly defined goals and sub goals. Beginning development early ensures technical issues are solved, avoiding delays if system requirements cannot be completely specified in advance, or scientific results that underpin the forecast products cannot be anticipated. More traditional software development lifecycles, such as the so-called 'waterfall' model, depend on system requirements and features being specified early and held static throughout the development period.

In the agile model of software development, regular user testing takes place at each development increment with user feedback incorporated into the next iteration. A test system may be made available for the use of developers and other project team members. An agile approach suits small and specialised project teams, as the flexibility of the agile approach may hold management risks for a larger development group. This approach enables responsiveness to the requirements of the end users of the system.

**Figure 9.** An example of a web-based tool providing seasonal climate forecasts.

### **6.2. A case study: The pacific seasonal forecast portal**

The development of web based tools integrating model-based outlooks with climatological information and other contextual information is one means of communicating information about climate risk to end users. [36]

A system was developed to ingest output data from the Predictive Atmospheric-Ocean Model for Australia (POAMA) GCM [17]. A user-facing component was developed, based on a rich web-based interface that provides a one-stop shop for access to dynamical modelbased outlooks. The purpose of the tool is to provide a specialised point of access to CGCM based seasonal outlooks for the national meteorological services of Pacific Island countries.

Managing Climate Risk with Seasonal Forecasts 581

Over the course of the project several workshops were held bringing representatives of partner countries together with scientists and service developers. These workshops provided training in the use and interpretation of dynamical seasonal predictions and introduced the software tools developed to provide the seasonal outlooks. Communication and training are essential elements in the development of outlook products: producing wellcalibrated outlooks is not effective unless the end users are equipped to use them correctly. A particular challenge is that of communicating seasonal forecasts that are couched in terms of probabilities, and which may differ from model to model. Both formal and informal user assessment of outlooks must be carried out to ensuring that what the forecast provider thinks is being communicated is what is being communicated. An iterative design process in which users are consulted early and often reduces the risk of miscommunication, and

In workshops scientists presented lectures on the physical basis of seasonal predictability; the historical skill of the POAMA GCM in predicting ocean and atmospheric conditions across the Pacific; software tools developed to provide access to the latest seasonal forecasts based on the coupled models. In one workshop participants also engaged in a series of exercises using the portal to generate seasonal outlooks for their local region which they described to the group in a series of successful presentations. Such hands on exercises are highly effective at developing skills in using seasonal forecasts and associated tools, in assessing the knowledge and level of engagement of participants, and in testing whether the

In discussions participants highlighted the importance of climate studies focused on improving the understanding of climate variability in Pacific Island Countries, noting that climate variability interacts with climate change leading to many of the first felt impacts of climate change. Improved knowledge of extreme climatic events, with the assistance of tailored forecast tools, will help enhance the resilience and adaptive capacity of

From the physical basis to the complexities of applications to specific industries and decisions it is clear that seasonal prediction is a large-scale enterprise requiring coordinated work across a range of scientific and technological disciplines. Steady improvements in GCM resolution and physics, coupled with ever increasing understanding of the physical mechanisms of predictability, will ensure that seasonal predictions become an important

allows for learning to proceed over a period of time.

tool works properly under realistic conditions.

**7. Conclusion** 

**Author details** 

Andrew Charles\*

Corresponding Author

 \*

communities affected by climate variability and change.

component of adaptation to a changing and more variable climate.

, Yuriy Kuleshov and David Jones *National Climate Centre, Bureau of Meteorology, Australian Government* 

This project was supported by the Pacific Adaptation Strategy Assistance Program (PASAP), a component of the International Climate Change Adaptation Initiative - an Australian Government Initiative of \$328 million over five years, 2008-2013 to assist with high priority climate adaptation needs in vulnerable countries in the Asia-Pacific region. As part of this program, the Australian Bureau of Meteorology lead a project to strengthen climate prediction capacities in the national meteorological and hydrological services of Pacific Island countries, including countries both north and south of the equator: Papua New Guinea, Tuvalu, Kiribati, Fiji, Marshall Islands, Federated States of Micronesia, Palau, Nauru, Cook Islands, Samoa, Tonga, Niue, Solomon Islands, Vanuatu and East Timor. A key element of this work was the development of a web-based application providing access to dynamical model-based seasonal outlooks. As previously described, one means to reduce vulnerability to climate change is by improve preparedness to anomalous climatic events.

Graphical displays of seasonal forecasts of broad-scale, point and climate driver forecasts are generated with an example shown in Figure 9. The web application displays the contextual information provided as meta-data by the data service layer, consumes the outputs of web services that produce figures and tables. It displays model-based outlooks as overlays on dynamical maps using geospatial web services. Access is given not just to application graphics but also to outlook data. User-friendly options for data extraction from the web portal are provided to support users of the range of tools from Excel to R.

An agile, iterative approach to the development of the web portal user interface (UI) included testing of early development versions of the portal with users at a project workshop, and in a series of country visits. These sessions validated the overall UI design and provided valuable feedback for improvements.

While much work went into the web front end, an equal amount of work was spent ensuring that the forecast generation layer is decoupled from this specific client. The access to the data provided by the data service layer allows for the future design of web clients that perform computational value adding using processing services, for example the ingestion and subsequent combination and calibration of multiple selected models.

The integration of data into a dynamical mapping tool provides opportunities for data mash-up in which data from different sources is displayed in composite. The provision of geospatial information in such a way that data from multiple sources can be integrated opens the way for new and interesting applications. For example, one potential future application for seasonal forecasting might be the display of agricultural or fishery yield data overlaid with outlook reliability data.

Over the course of the project several workshops were held bringing representatives of partner countries together with scientists and service developers. These workshops provided training in the use and interpretation of dynamical seasonal predictions and introduced the software tools developed to provide the seasonal outlooks. Communication and training are essential elements in the development of outlook products: producing wellcalibrated outlooks is not effective unless the end users are equipped to use them correctly. A particular challenge is that of communicating seasonal forecasts that are couched in terms of probabilities, and which may differ from model to model. Both formal and informal user assessment of outlooks must be carried out to ensuring that what the forecast provider thinks is being communicated is what is being communicated. An iterative design process in which users are consulted early and often reduces the risk of miscommunication, and allows for learning to proceed over a period of time.

In workshops scientists presented lectures on the physical basis of seasonal predictability; the historical skill of the POAMA GCM in predicting ocean and atmospheric conditions across the Pacific; software tools developed to provide access to the latest seasonal forecasts based on the coupled models. In one workshop participants also engaged in a series of exercises using the portal to generate seasonal outlooks for their local region which they described to the group in a series of successful presentations. Such hands on exercises are highly effective at developing skills in using seasonal forecasts and associated tools, in assessing the knowledge and level of engagement of participants, and in testing whether the tool works properly under realistic conditions.

In discussions participants highlighted the importance of climate studies focused on improving the understanding of climate variability in Pacific Island Countries, noting that climate variability interacts with climate change leading to many of the first felt impacts of climate change. Improved knowledge of extreme climatic events, with the assistance of tailored forecast tools, will help enhance the resilience and adaptive capacity of communities affected by climate variability and change.
