Weather, and its long-term manifestation, urban climate, is a key element of the quality of life in a city.
While, at first glance, weather seems a "given", it is the capability to adapt -
both in the short term to extreme events as well as in the long term to climatic patterns -
that makes analyzing and predicting weather important and useful
for the design and management of a city.
- Weather and climate, and specifically extreme events can contribute to urban stress, and affect public health
and well being.
- Weather and climate also affect the energy demand of a city for heating and cooling.
- Weather and climate are key driving factors for the dispersion of air pollutions,
and the formation of ozone from organic substances and oxides of Nitrogen;
- Weather and climate strongly affect water resources, both in terms of the supply
available to a city, but also the demand;
- Extreme precipitation events can cause urban flooding, affect the efficiency of the drainage systems.
While weather is a more regional phenomenon, cities due affect weather locally:
heat islands due to changes in albedo on evapotranspiration,
changed air flow and ventilation, and a modified water cycle all contribute to city specific modifications.
The prediction of larger scale weather is by now fairly reliable; the prediction of very small scale phenomena
in the city extremely difficult to impossible. One approach is to use ensemble of forecasts,
the heuristic modifications to the regional forecast based on urban structures, energy use,
building, surfaces, land cover.
In CityWare meteorological forecasts (using the prognostic models MM5 and/or WRF)
generates key indicators on extreme events, but also drives other model such as air quality, and water resources.
The continuous forecasts are also used as the basis for the prediction of any emergency that is affected by
the weather, such as dispersion of smoke from a fire, or a forest fire in and around the city.
Key output variables are estimates for min/max temperature, precipitation, wind speed,
but also humidity and pressure (change), combined with the corresponding monitoring data.
To improve forecasts, CityWare can use (super) ensemble forecasts generated with both models
over a range of input conditions (NCEP GFS global forecast scenarios)
and optional data assimilation (nudging of initial conditions) to estimate the probability of certain extreme events.