Smart Cities and the Weather

A study by the US National Center for Atmospheric Research (NCAR) in 2008 found that the impact of routine weather events on the US economy equates annually to about 3.4% of the country’s GDP (about $485 billion). This excludes the impact of extreme weather events that cause damage and disruption – after all, even “ordinary” weather affects supply of and demand for many items, and the propensity of businesses and consumers to buy them. NCAR found that mining and agriculture are particularly sensitive to weather influences, with utilities and retail not far behind.

A moment’s thought will confirm that many city systems are likewise routinely influenced by the weather – systems ranging from energy and water, to sanitation, transportation, healthcare, parks and recreation, policing and so on. Add in extreme weather events, and the list grows to include event/disaster forecasting, preparation and response.

Many of these, disaster management included, are the focus of smart city innovations. Not surprisingly, therefore, as they seek to improve and optimize these systems, smart cities are beginning to understand the connection between weather and many of their goals.  A number of vendors (for example, IBM, Schneider Electric, and others) now offer weather data-driven services focused specifically on smart city interests. For example:

  • In the energy field, prediction of demand, renewable energy yield, and storm-related outages, to enable grid configuration and management, including demand-response management;
  • With water systems, prediction of demand, management of irrigation activity and control of such tasks as water aging and blending, chemical dosing, and waste water treatment;
  • In transportation, adaptive traffic controls and routing, drive time estimates, fleet disposition and management, and road safety;
  • In environmental management, prediction of air pollution (ozone, particulates, NoX) and water pollution;
  • With smart buildings, inputs for adaptive HVAC and free-air cooling;
  • Prediction and management of disruptions, for example from rainfall or snow – and at the other end of the range, forecasting severe weather events that lead to risk and damage.

The underlying trend here mirrors what is happening in cities themselves.  First, as with smart cities, it’s about the Internet of Things and the impact that it has on weather forecasting capabilities.  Satellite data is one part of this, but terrestrial data is a huge component also.  IBM’s Weather Company alone uses over 250,000 weather stations globally, many of them from its citizen-sensing network, the Weather Underground; and it processes over 100 terabytes of 3rd party data per day.   Southwest Airlines’ planes now detect atmospheric water vapor levels for NOAA. The Con-Way haulage company collects weather data on its trucks as part of the US MesoNet program. I cannot prove it, but it seems reasonable to suppose that collectively, weather data collection may be one of the largest uses of the IOT to date, in terms of both the number of collection points and the volume of data.

Second, and as with smart cities, it’s about the ever-growing power of analytics and AI.  Weather models have long been among the major uses of super-computing resources. However, the ability today to create high resolution or micro-forecasts (at a scale as focused as 0.2 miles/0.5km) effectively takes weather forecasting to a whole new level of capability and application, especially when combined with other data on topology, traffic, vegetation and so on. In my previous blog post, I mentioned just such an example of this with the AI-powered air pollution forecasting system in Beijing that differentiates pollution levels on a micro-scale; a similar level of precision micro-forecasting, when combined with other data, makes it possible not just to forecast output from wind-farm or solar installation many hours in advance, but for larger installations output by zone within the installation, at >90% levels of accuracy.

Article originally published here.