The operation of many cities is dependent to a significant degree upon weather conditions, especially with regard to relative extremes in wind, precipitation or temperature. For example, with precipitation events, local topography, surface characteristics and weather conditions influence water runoff and infiltration, which directly affect flooding as well as drinking water quality and availability. The impact of such events creates issues of public safety for both citizens and first responders. Therefore, the availability of highly localized weather model predictions focused on municipal public safety operations has the potential to mitigate the impact of severe weather on citizens and local infrastructure. Typically, information at such a scale is simply not available. Hence, what optimization that is applied to these processes to enable proactive efforts utilizes either historical weather data as a predictor of trends or the results of continental- or regional-scale weather models. Neither source of information is appropriately matched to the temporal or spatial scale of many such operations. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature it is only directly suitable for reactive response.

To understand the business implications of these ideas, IBM Research has an on-going project, dubbed “Deep Thunder”. In particular, the ability to predict specific events or combination of weather conditions with sufficient spatial and temporal precision, and lead time coupled to the operational impacts is being addressed to enable proactive allocation and deployment of resources (people and equipment) to mitigate the effects of severe weather and increase time for planning.

For example, the city of Rio de Janeiro, in Brazil, often faces the consequences of intense rainfall, which include landslides and flooding. In early April 2010, the city endured one of the worst torrential rainstorms in decades. At least 110 people were killed and tens of thousands lost their homes. To assist in planning for such events in the future, the city's leaders have enabled sophisticated capabilities for the coordinated management of disasters, emergencies, or planned events of importance. As part of that effort, the integration of advances in hydro-meteorological research is a key prerequisite. Given the geography of the city, such capabilities have significant challenges. In addition to its near-tropical setting along the coast of the Atlantic Ocean and the western portion of Guanabara Bay, there are regions where the terrain has a high aspect ratio, related to the Sierra do Mar mountains. Although sea breezes moderate the temperatures along the coast, especially during the summer, cold fronts from the Antarctic can lead to rapid changes in local weather. Of particular concern is the rainy summer season from December to March, during which O(100mm/day) precipitation events occur. Given the complex terrain and surface characteristics, significant flooding becomes likely during this period.

Current state-of-the-art numerical weather prediction (NWP) codes operating at the meso-gamma scale have been shown to have potential in predicting specific events or combination of weather conditions with sufficient spatial and temporal precision to address the aforementioned scale mismatch. Therefore, the WRF-ARW (version 3.2.1) community NWP model was adapted for use in the Rio de Janeiro area. An operational configuration was developed by retrospective analysis of recent significant precipitation events and compared against data from a network of 32 rain gauges operated by the city government during that period. Those results coupled with throughput considerations for availability of data for initial and boundary conditions as well as computational resources led to a configuration of four two-way nests focused on the Rio de Janeiro metropolitan area at 1km horizontal resolution. To address the influence of the complex terrain, 65 vertical levels were established with typically the lowest 15 or so being within the planetary boundary layer. The model orography was developed from altimetry data at 90m resolution available from the NASA Shuttle Radar Topography Mission. Data at 0.5 degree resolution from the NOAA Global Forecasting System are used for initial conditions and lateral boundaries. The configuration also has parametrization and selection of physics options appropriate for the range of geography in the region. It included the use of a sophisticated, double-moment, 6-class, explicit cloud microphysics scheme. This configuration was placed into operations in May 2011, producing 48-hour forecasts every 12 hours. The results of each model-based forecast are provided to the Government of Rio de Janeiro via a web portal in their integrated command center, which has been dubbed in Portuguese as Previsão Meteorológica de Alta Resolução (High-Resolution Weather Forecast) or PMAR. It includes HDTV-resolution animations of various two- and three-dimensional visualizations of key weather variables, specialized meteograms at locations of key landmarks, weather stations, etc. within the city and detailed tables of weather data at those locations. All of the web-based content contains information every 10 minutes of forecast time for each 48-hour model run. The visualizations are customized to the model configuration and the requirements of the end users, and incorporate data from the city's geographic information system.

We will discuss the approach and the background research effort, some specifics of how we brought the solution into an operational phase, and lessons that were learned through the development and deployment. The work is on-going and the model results are being evaluated. We are also developing extensions to couple the precipitation estimates from the NWP system to a highly detailed hydrological model to provide predictions of water accumulation to further assist in planning for flooding events. We will present how the forecast information is being used and discuss the overall effectiveness of our approach for these and related applications as well as recommendations for future work.