High resolution real-time weather forecasting

With over 50% of the world population living in cities and a projected two-thirds of the population living in cities in 2030 (UN-Habitat), accurate weather forecasting becomes an important tool to respond timely and mitigate risks in cities. Extensive conurbations like the Pearl River Delta, Tianjin-Beijing, Yangtze River Delta, New York-Boston and (mega) cities like Tokyo, Sao Paulo, Jakarta, Manila, Los Angeles, Lagos, London, Hanoi, Bangalore have important features in common: dense populations, impervious built surfaces, significant emissions of pollutants, heat and waste, etc.(WMO). Large urban areas have differentiated weather patterns distributed across the city or metropolitan area. High resolution real-time weather forecasting becomes ever more important in order to forecast impacts, to communicate timely to urban populations at risk and to take right decisions in deploying emergency services in cities. It can also provide the evidence for adaptation measures among others the location of flood retention areas or the implementation of smart sewage systems that can be controlled as needed. High resolution weather forecasting can also provide diversified data on energy consumption and production of different neighbourhoods in the city and the way smart grids should respond to distributed peaks. In an urbanised world the weather forecast can no longer be seen as an external factor as the urban atmospheric conditions are impacted by emissions, pollution, heat island effects, urban form and other environmental factors. High resolution weather forecasting is increasingly focusing on air quality in addition to temperature, humidity and precipitation which is a signal that urban meteorology, climate and environmental research could evolve in more integrated city services (Urban Climate, Baklanov, Grimond). High resolution real-time weather forecasting for urban areas is a field that requires not only the technical instruments, data collection and interpretation, but also sophisticated comparative analysis between urban datasets available in cities, accurate algorithms, policies and governance models for risk mitigation.
Picture: Antony Pratap CC2.0

Algorithmic Transparency

“Algorithmic governance is made possible by vast increases in computing power and networking, which enable the collection, storage, and analysis of large amounts of data. Cities seek to harness that data to rationalise and automate the operation of public services and infrastructure, such as health services, public safety, criminal justice, education, transportation, and energy. The limitations of local government make private contractors central to this process, giving rise to accountability problems characteristic of policy outsourcing.” (2018, Brauneis, Goodman) Algorithmic governance will be increasingly important in the way decisions are made in cities. This has led to a debate on the transparency of the algorithms and the potential biases built into it. As discussed in the Right Way to Regulate Algorithms: “The purpose of data-driven algorithms like this one is to make policing more objective and less subject to individual bias. But many worry that the biases are simply baked into the algorithms themselves.” New York will be the first city that will scrutinise the potential biases in algorithms and that will develop policies on how to regulate access to underlying assumptions. According to the New Yorker: “Once signed into law by Mayor Bill de Blasio (dec 2017 red.), the legislation will establish a task force to examine the city’s “automated decision systems”—the computerised algorithms that guide the allocation of everything from police officers and firehouses to public housing and food stamps—with an eye toward making them fairer and more open to scrutiny.”

Sources:
Algorithmic Transparency for the Smart City, Robert Brauneis & Ellen P. Goodman
The Right Way to Regulate Algorithms, by Chris Bousquet, Stephen Goldsmith
The New Yorker: New York City’s Bold, Flawed Attempt to Make Algorithms Accountable
Picture: Kolitha de Silva CC BY 2.0