GURME Terms of Reference

  • Address the research barriers to advance the predictive capacity at increasing resolutions, and in the urban context in particular: through the coordination of reviews in the current state of science in urban-scale forecasting and associated monitoring, establish activities where gaps exist.
  • Develop activities on those research questions/issues that transcend disciplines and require leveraging a broader community to develop improved forecasting concepts and tools to resolve complex urban environments at increasing scales; facilitate data sharing and establishment of test beds.
  • Given the integrative nature of modelling, the on-going scientific trend towards seamless predictions and the evolution of technology, actively engage other WMO advisory and working groups within WWRP, GAW and the rest of its organisation, to address this complex and multidisciplinary challenge. 
  • While megacities will continue to receive particular attention, orient its research to cover the full array of urban environments that are key to the broader scientific question of urbanscale modelling. GURME Terms of Reference Page 2 – August 2, 2016
  • Continue to nurture its engagement with the health community as the main partner in assessing the needs, evaluating the benefits and communicating resulting services to society within these urban environments.
  • Build capacity through its research projects, identifying those environments that constitute gaps in the overall directions of the GURME program and Encourage in its projects the development and testing of derived services. The products themselves would take the form of forecasts, alerts and warnings and/or realtime/NRT maps or databases. 
  • Forge stronger collaborations with CBS and/or individual operational centres to transition products in dissemination systems in a form that is well suited for large or targeted audiences.
  • Collaborate explicitly with the Apps-SAG on projects at the interface of regional and local scales and contribute actively to facilitating data assimilation efforts focused on integrated/coupled models and at finer and finer scales.