1. How can new capabilities for urban observations be developed and implemented, particularly using the network of personal digital assistants (PDAs; including smartphones) and vehicles and new technologies for measurements in the whole planetary boundary layer?

2. How can weather and climate models be urbanized and how can urban areas be included in the model prediction evaluation and validation metrics?

3. How will the capability for integrated urban meteorology-decision support systems be developed?

chemical or radioactive material in an urban area. Another example is how the construction sector uses historical data regarding the occurrence of heavy rain, snow, and icing to prepare for days in which no outdoor work can take place due to such precipitation events. A clear mechanism to help the urban meteorological community better identify user groups, reach out to them, and begin an ongoing dialogue to assess and better meet their needs has yet to be identified.

It is important to recognize that there are multiple types of urban meteorological phenomena that have impacts on different types of users with different types of needs. End users are heterogeneous and cover a vast spectrum of job roles, goals, needs, experience, and understanding. Their needs span a wide range of accurate urban meteorological information, from high frequency and low frequency events that may occur over the short and the long term. To complicate long-term planning, both types of events will be affected by climate change. Acknowledging and understanding this heterogeneity is important for the urban meteorological community to better understand, interact with, and meet the needs of end users.

As many participants noted at the workshop, there are numerous end user needs that are not sufficiently being met (Table S.1). In many cases, the urban meteorological community is simply unaware of the precise data and information needs of various user groups. If such information is not provided, there is a risk that disparate groups of end users will start generating (or more fully develop) their own data streams, not necessarily following best practices in data collection, analysis, or interpretation, and produce not only redundant but also inconsistent information. More importantly, if the urban meteorological community does not provide required information, end users’ needs will not be met, reducing the effectiveness of their decision-making.

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