Hyperlocal Downscaling using WRF: Science, Technology and Applications

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Monday, 5 January 2015: 2:15 PM
124B (Phoenix Convention Center - West and North Buildings)
Wallace Hogsett, Weather Analytics, Bethesda, MD; and S. Cecelski and R. Rogers

Numerical weather prediction (NWP) is an integral part of both daily weather forecast operations and weather-related decision-making in industries such as energy, insurance, and agriculture. Various NWP systems provide information about the past, current and future atmosphere at a finer spatial scale than in-situ observational networks, and in areas where no in-situ observations exist. The National Weather Service maintains a number of high-quality NWP systems, which vary in many ways, including spatial coverage, grid resolution and forecast length. Global NWP systems are spatially comprehensive, but cannot capture small-scale phenomena that are of significant interest to weather-sensitive sectors of the economy. On the other hand, the finer resolution NWP systems can capture some smaller-scale phenomena (e.g., thunderstorms, topographic variations, air-sea contrasts), but are spatially limited. The work presented here will describe efforts to leverage available NWP datasets and emerging computing technologies to develop a high-resolution, global historical database of high-impact events.

Weather Analytics has begun to construct a continuously updated, hyper-local database of weather events at a resolution higher than is produced by any operational NWP or analysis system. Using the advanced research version of the Weather Research and Forecasting Model (WRF-ARW), cloud-based computing infrastructure, and a dynamical data assimilation method, a number of high-impact variables are created through a downscaling process. By utilizing a multi-dimensional nudging data assimilation system with a quality-controlled set of several observation types, downscaling to 1 km resolution captures well the actual atmospheric state when compared to available observations. The hyper-local simulated data is then merged with various observational data to produce a global historical dataset that enables verification and understanding of past events. With the database as the foundation, several products are under development to inform “weather intelligent” business decisions.