Development of a global data server using downscaling techniques to produce high-resolution, long-range, hourly forecast data
In key locations where very high resolution forecasts are needed, such as the continential United States, Europe and Asia, hourly forecast variables from a Super MicroCast model of 12 km resolution are downscaled to a resolution of 1 km, out to 60 hours. High-resolution datasets of land usage and elevation are taken into account in the downscaling process, greatly improving forecast accuracy in these regions. Accuracy is further achieved for surface temperature by applying MOS correctors to the 1 km temperature data in order to correct any systemic model biases. Beyond 60 hours, blending techniques are performed on almost all model variables to transition smoothly from the 1 km resolution short-range model to the longer-range GFS model of lower resolution. Blending also occurs for the transition between the GFS and GFS Ensemble beyond 180 hours.
Weather Central recently launched the DataCloud API allowing users to programmatically access the real-time updating weather datasets provided by the global data server. When a user requests a forecast for a specific latitude and longitude, the data server determines which model forecast to return according to the highest resolution data available for that site. The API returns the forecast in xml or image-tile format, per the user's request. This session will feature a detailed discussion of the Weather Central global data server followed by a brief discussion of the DataCloud API.