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This evaluation of science in support of weather prediction showed that there is a need and a legitimate role for EC to conduct scientific activities in support of weather prediction. Weather prediction is a science-based activity relying on science research and continuous improvements as essential functions of the weather production innovation chain. Such science is essential to support the provision of accurate and timely weather information that enables other government service providers and Canadians to make informed decisions that protect their health and safety against changing weather conditions. The science also contributes to meeting EC’s departmental mandate to provide meteorological services and to supporting the mandates of several federal stakeholders. As well, science is needed by private sector stakeholders to ensure the safety and efficiency of their operations. Finally, active involvement in cutting-edge research is required for the MSC to uphold its international obligations as a member of the WMO and to maintain its highly interdependent collaborative relationships with other countries’ meteorological services. In light of these findings, science activities in support of weather prediction are relevant to current needs and government priorities. Furthermore, future science innovations are likely to remain relevant and necessary in order to address evolving client demands, which are in part prompted by science innovations being developed not only within MSC but also by other countries worldwide.
The performance of the science activities in support of weather prediction was measured in terms of the extent to which its end users’ needs are being met, the effectiveness of its science knowledge transfer along the weather prediction innovation chain, and the adequacy and effectiveness of its priority‑setting and decision‑making mechanisms.
In order to deliver improved services to external clients, those involved in the delivery of weather prediction need to be part of an integrated innovation chain, supported by the appropriate infrastructure and processes as well as collaboration from the global meteorological community. The needs of external clients of weather prediction are diverse, expanding and becoming increasingly sophisticated as a result of increasing possibilities due to evolving science and technology.
Evidence showed that EC’s weather prediction service is generally viewed as providing high‑quality information and products, and relying on a wealth of knowledge and expertise. EC is also recognized among the international meteorological community as a valued contributor to scientific collaboration and a world leader in a number of key areas. Traditional weather services are working well and continue to improve. However, evidence indicates that more could be done to enhance the weather prediction products and the mechanisms of delivery, in order to increase access and better support the evolving decision-making needs of external clients.
Key literature highlights the importance of a science‑based function with "a collaborative research approach spanning the entire [knowledge to action] process" whereby "[r]esearchers and decision makers are engaged together from initially identifying the research question through to applying the knowledge."93 Departmental stakeholders involved in the delivery of weather predictions clearly identified with being part of an innovation chain, and recognized the benefits of collaboration both with internal colleagues and with the broader science community domestically and internationally. Internally, the interaction between research, development, production and monitoring functions appears strong, but more could be done to strengthen communications between these roles and the service function in order to ensure that the external client perspective is adequately considered in science decisions, and to ensure that the service function can have an improved understanding of emerging science to help anticipate what may be possible in the future. A gap was also identified in the area of transferring science developed in the MSC’s national laboratories into operations, and in ensuring that information is shared in a manner that allows it to effectively support the decision-making needs of the Department’s diverse group of clients. These findings indicate that, in order to maximize the effectiveness of weather prediction activities, some improvements are needed to the transfer of science knowledge between those involved in developing such knowledge and those primarily tasked with transferring end products to weather prediction clients.
Several mechanisms are in place for weather prediction priority setting and decision making. These mechanisms appear to generally function well and recent improvements, such as the creation of MEPIC (co-chaired by DGs from the MSC and S&TB), CIOB representation on the WES Board, and the development of signature projects to pursue senior management’s vision for the future of the MSC, are appropriate means to address ongoing needs for improved coordination and for integration of the S&TB and CIOB support functions in the overall weather prediction science priority‑setting and decision‑making process. The need remains, however, for more extensive communication throughout the Department of senior management’s vision and priorities for allocation of limited departmental resources to those involved in the delivery of weather predictions.
Available evidence suggests that the level of effort expended for weather prediction activities, including science, yields commensurate or better value for given resources and that the delivery models adopted are appropriate. The automation of weather forecasts was highlighted as a key change introduced by the MSC that has resulted in some increased efficiencies. Furthermore, the recent introduction of the QMS shows early evidence of benefits that will likely, in the near future, outweigh the investment of time and resources required to implement and maintain it. Benefits observed to date include articulation of roles and responsibilities, mapping of client/supplier relationships and dependencies, documentation of processes, and reporting on performance. These products are important tools to support efficient and effective decision making and knowledge transfer. Also, the QMS approach overall contributes to promoting a performance‑measurement culture, which is a key contributor to continuous improvement. This evaluation evidence leads to the conclusion that the implementation of the QMS is beneficial for the organization. Also, there were no clear opportunities for efficiency improvements or alternative modes of delivery for EC’s weather prediction science identified as part of this evaluation.
The effectiveness, efficiency and sustainability of weather prediction activities are, however, influenced by a number of external factors. Serious concerns were expressed by departmental stakeholders and confirmed by external reviews (e.g., CESD audit of severe weather warnings) with regards to the Department’s limited capacity to maintain and upgrade its rapidly aging monitoring infrastructure and its supercomputer’s limited capacity to meet the demands of all internal and external clients. These factors impact the ability of weather prediction science to respond to weather prediction client needs. These observations lead to the conclusion that the MSC’s ability to continue to be responsive to client needs and to maintain its current level of service are largely dependent on factors that are outside of its direct control.
Three main, interrelated areas for improvement therefore emerge from the evidence gathered as part of this evaluation. First, in light of evolving demands for weather prediction products and services and the limited availability of resources, the Department should identify priorities in terms of client groups to serve and specific client needs to address (i.e., specific products to develop and levels of services to offer). Such strategic choices will have a significant influence on the type of science activities pursued by the research, development, production and monitoring functions.
Second, the identification of departmental priorities should be done in consultation with members of all functions along the weather prediction innovation chain. Priority setting should consider not only the external clients’ needs but also the need to remain at the cutting edge of scientific research in order to maintain Canada’s strategic positioning in the top‑tier group of countries providing meteorological services, and in order to foster the innovation needed to remain relevant to end-users.
Third, the priorities, once established, should be clearly communicated throughout the innovation chain in order to effectively support priority setting and decision making within each functional group, from research to service, including monitoring and IM/IT. Furthermore, ongoing communication and interaction between all these functional groups should be maintained in order to ensure effective knowledge transfer.
93 Graham et al., "Lost in Knowledge Translation," p. 17.